Abstract:
The present invention is related to a system and method of considering time segments or intervals in a collaborative filtering model. The present invention extends collaborative filtering approaches by integrating considerations of temporality into the training and/or vote input associated with the usage of collaborative filtering models. The present invention also applies filtering to the output with temporal models, so as to view a most appropriate subset of recommended content, centering on content that may be available at a target time. The present invention applies time to a collaborative filtering model by allowing weight to be associated with selections within a current time segment, selections historically watched within the current time segment by the user and selections historically watched within the current time segment by a large group of users.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a divisional of co-pending U.S. patent application Ser. No. 09/825,820, entitled “TIME-CENTRIC TRAINING, INFERENCE AND USER INTERFACE FOR PERSONALIZED MEDIA PROGRAM GUIDES”, filed Apr. 4, 2001. The entirety of the above-noted application is incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present invention relates to information retrieval and processing, and more particularly to a system and method for providing selected information as a function of a user&#39;s previous reviewing habits over different time periods. 
     BACKGROUND OF THE INVENTION 
     Recently, there has been an increasing trend of the amount of information available on any given subject matter due to the interconnection of computers via networks (e.g., the Internet) and the increased availability of inexpensive data storage. In many situations, people attempting to retrieve information on a subject area are overwhelmed with a vast amount of information. Data that is desired becomes difficult to locate amongst the vast amount of information presented to a user. Various known information retrieval systems have evolved that attempt to avoid the problem of information overload by performing a ranking or prioritization of information. These systems attempt to retrieve and provide information based on an approximation of how useful, interesting, and/or responsive the information is likely to be to a system user. 
     For example, many systems provide search engines, which search database contents or “web sites” according to terms provided by a user query. However, limitations of search heuristics often cause irrelevant content to be returned in response to a query. Furthermore, the vast wealth of available information makes it difficult to separate irrelevant content from relevant content. Other systems organize content based on a hierarchy of categories. These systems suffer from the fact that a user may select a category to navigate through and determine that the content of the category is of no interest to the user. The user must then backtrack through one or more of the hierarchical categories to return to the available categories. The user then will have to continue this process until the user locates the desired information. 
     In view of the shortcomings of the systems discussed above, collaborative filtering systems have been developed. Collaborative filtering methods center on the construction of models that can be used to infer preferences of individuals or groups by considering the actions of a large groups of users. Collaborative filtering systems predict preferences of a user based on known attributes of the user as well as known attributes of other users. For example, a preference of a user may be whether they would like to watch a particular television show, while an attribute of the user may include their age, gender and income. In addition, the attributes can contain one or more of the user&#39;s known preferences, such as the user&#39;s dislikes of certain other shows. A user&#39;s preference can also be predicted based on the similarity of that user&#39;s attributes to other users. Typically, attributes are provided numerical values (e.g., a vote) and a weighted sum of the attribute values are utilized to determine a preference. Additionally, correlation computations are employed for a given user and other users to predict the preference of a user for a particular selection. Some collaborative filtering systems employ clustering algorithms to determine users whose preferences seem to be similar. 
     Collaborative filter systems have been determined to be relatively successful in selecting desirable preferences of a user given adequate attributes of the user. However, in certain circumstances a single content-access system may be employed that is utilized by multiple users all having different attributes and preferences. For example, a single household may all share a single computer or a single television device. In these circumstances, there is no way for a collaborative filtering system to determine which of the household members is utilizing the system at any given time without the member identifying themselves. Accordingly, there is an unmet need in the art for providing a collaborative filtering technique to an information retrieval and processing system that can be employed on a shared device. 
     SUMMARY OF THE INVENTION 
     The present invention is related to a system and method of considering time segments or intervals in a collaborative filtering model. The present invention extends collaborative filtering approaches by integrating considerations of temporality into the training and/or vote input associated with the usage of collaborative filtering models. The present invention also applies filtering to the output with temporal models, so as to view a most appropriate subset of recommended content, centering on content that may be available (e.g., via a broadcast) at a target time. The present invention applies time to a collaborative filtering model by allowing weight to be associated with selections within a current time segment, selections historically watched within the current time segment by the user and selections historically watched within the current time segment by a large group of users. A subset of selections based on the model is then provided to a user during a given time segment. 
     The system and methods have particular application to television viewing systems for providing users or groups of users with a subset of available viewing selections that may be preferred by the users employing not only the users&#39; viewing habits, but also the users&#39; viewing habit over particular time intervals (e.g., early morning, late morning, early afternoon, late afternoon, primetime, late night). The present invention provides for improved probability predictions that a user or group of users will want to review content based on a user&#39;s or a household&#39;s viewing habits over one or more particular time segments of the day or week. The present invention applies time to a collaborative filtering model by considering the time at which content is viewed in a household or site. 
     Several related methods have been developed for extending collaborative filtering to consider the time of day and/or portion of the week of desired content and/or allow for modifications of the recommendations of the collaborative filtering analysis. In one approach, votes or preferences information about desired content are considered in an explicit or implicit manner by considering votes for media content that has been viewed within the time segment or portion of week that is currently active or being considered, and to also optionally allow user&#39;s to filter recommendations over different time periods by changing the target of the recommendations (e.g., moving from next hour to next three hours, or in 3 hours from now, etc.). 
     In another approach, distinct models are constructed for different portions of time from data gathered from multiple users. Such time-centric collaborative filtering allows a system to cast votes based on the previous history of viewing at a site and to employ information about the content review activity of a large group of users to generate recommendations. A subset of selections based on the model is then provided to a user or group of users for a desired target time segment. 
     One aspect of the invention relates to applying the time segmented collaborative filtering model to selections approximately in real time. The present invention model provides for predictions of selections within a present time period of a given time segment. For example, the present invention solves the problem of the situation with a broadcast television show that is turned on that is already in progress. It also covers support of users with recommendations of media that will be broadcast within some specified time segment in the future, by considering votes and/or appropriate models for the time segment of interest, in conjunction with the availability of media content at a specified time. The time-centric recommendation system can be coupled with an electronic (e.g., potentially online) program guide, so as to produce a program guide for a specified segment and/or span of time that is dynamically filtered based on a user&#39;s preferences as inferred by employing a collaborative filtering model, potentially considering appropriate votes (e.g., for the time segment under consideration). 
     To the accomplishment of the foregoing and related ends, the invention then, comprises the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative embodiments of the invention. These embodiments are indicative, however, of but a few of the various ways in which the principles of the invention may be employed and the present invention is intended to include all such embodiments and their equivalents. Other objects, advantages and novel features of the invention will become apparent from the following detailed description of the invention when considered in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a block diagram of a system utilizing an inference system based on a first collaborative filtering model in accordance with one aspect of the present invention. 
         FIG. 2  illustrates a block diagram of a system utilizing an inference system based on a second collaborative filtering model in accordance with one aspect of the invention. 
         FIG. 3  illustrates a block diagram of a system utilizing an inference system based on a third collaborative filtering model in accordance with one aspect of the invention. 
         FIG. 4  illustrates a block diagram of an inference system being employed in a multimedia viewing system utilizing an electronic program guide in accordance with one aspect of the present invention. 
         FIG. 5  illustrates a block diagram of an example of possible ranked program sets in accordance with one aspect of the present invention. 
         FIG. 6  illustrates a block diagram of a possible user interface of an electronic program guide in accordance with one aspect of the present invention. 
         FIG. 7  illustrates a block diagram of a program delivery system residing on a server coupled to a set top box having an electronic program guide system in accordance with one aspect of the present invention. 
         FIG. 8  illustrates a flow diagram of a methodology for training a collaborative filtering system in accordance with one aspect of the present invention. 
         FIG. 9  illustrates a flow diagram of a methodology for providing a list of ranked general recommendations of employing the trained collaborative filter system in accordance with one aspect of the present invention. 
         FIG. 10  illustrates a flow diagram of a methodology for identifying and training cluster specific collaborative filtering systems and providing ranked recommendations of titles based on cluster specific preferences in accordance with one aspect of the present invention. 
         FIG. 11  illustrates a block diagram of a computer system in accordance with an environment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. The present invention is described with reference to a system and method for considering time segments or intervals in a collaborative filtering system. The present invention has particularly useful application to an electronic program guide system, such that recommendations of media selections most likely to be watched by a user of the system are provided based on the time interval or time segment of the selections. The collaborative filtering techniques of the present invention allow for the system to predict the likelihood that a user will prefer a program regardless of the user&#39;s genre by evaluating the time interval that is being viewed in a multi-user system. 
     The present invention employs collaborative filtering models or systems. The collaborative filtering models or systems of the present invention are built via learning procedures from user log data that captures the history of the review of items (e.g., media content, such as television shows) by large groups of users. Once constructed, the collaborative filtering models are used to make recommendations by inferring preferences of users. Recommendations can come in several forms including the display of a single item or list of items and the filtering and display of a custom-tailored program guide. Recommendations for a time-centric collaborative filtering system can be targeted at some specific target time or target time period. The target time period can be now, the next hour, the next two hours, or two hours from now, etc., depending on what a user has asked for and what the default is in situations where a user has not specified any specific time period. 
     In real-time use, a target time period is identified, and appropriate information or evidence about a user&#39;s preferences are input into the collaborative filtering model that was constructed from the user log file(s). The evidence about preferences is typically referred to as votes. Evidence or voting can take many forms, including explicit input of samples of preferred items by users (e.g., some systems ask the user to input votes that a user likes and then inputs these to the previously constructed collaborative filtering model). Evidence of preferences also includes the gathering of implicit evidence of items that are liked or disliked via monitoring of a user&#39;s activity. For example, evidence of a user&#39;s preferences can be gathered implicitly by monitoring content that the user has dwelled on for some threshold amount of time-taking the dwell time as a surrogate of interest. Interest can also be inferred by monitoring items that the user has requested to be recorded. A user reviewing an item over a threshold period of time can be an indication that the user is interested in the item and similar type items. Furthermore, a brief review and jump away from a selected item to other content can be inferred to as a dislike, or negative vote. 
     The present invention also allows users to explicitly modify votes being considered by giving them a means of adding items or removing them by editing a list of votes. The evidence or votes about a user&#39;s, site&#39;s, or household&#39;s preferences considered is the history, recent or long-term, of items reviewed by the household. A reviewed items list includes the positive and/or negative votes that are implicitly monitored by looking at patterns of dwell time and jumping. The reviewed items list can be limited by any number of filtering techniques. For example, a time-centric reviewed items list can be provided by tagging the reviewed items by time period when they are reviewed, and only considering the shows that have tags consistent with the target time period when recommendations are made. The reviewed items list can capture the history of review of information content at a site including the pooling of history data from multiple systems at a site or considering only a single system for some threshold period of time. 
       FIG. 1  illustrates an example of a system  10  utilizing a collaborative filtering system  18  according to one aspect of the present invention. In a training mode, the collaborative filtering system  18  utilizes selections of previously viewed information that is logged in a user&#39;s database  12  and selections of previously viewed information that is logged in databases  16  of other shared systems. It is to be appreciated that the database information may be stored locally at the system  10 , remotely at a remote server or the like (e.g., in a single database) or across a number of servers. Additionally, the collaborative filtering system  18  may reside locally at the system  10 , remotely at a remote server or across a number of servers. The collaborative filtering system  18  is trained by examining selection reviewed by a large pool of users for more than a threshold amount of time. Negative votes are also considered by monitoring selections jumped away from after being reviewed briefly. Temporal filtering can then be employed to provide a reviewed item list and a recommendation list for a particular time segment. 
     Title selections are time stamped with different time segments by event type (e.g., title). The log data is annotated with distinctions about the time of the day that a system user has viewed the information. For example, a day can be segmented into early morning, mid-morning, afternoon, late afternoon, early evening, evening, late night. Additional segments can include information that is viewed on weekdays versus weekends. Furthermore, non-weekdays can be further subdivided into Saturday, Sunday and holidays. Elements from a dataset are considered tagged with these time segments or intervals and trained with this data employing conventional collaborative filtering techniques. Various collaborative filtering techniques may be employed to carry out the present collaborative filtering model (e.g., correlation techniques, Bayesian machine learning, probability tree networks, etc.). 
     In real-time, a target time period is identified by a user interface  22  and appropriate information or evidence about a user&#39;s preferences is inputted into the collaborative filtering system  18 . Evidences of preferences include information from previously selected information from the database  12 , other attributes of the shared system  14  and explicit input of samples of preferred items by the user through the user interface  22 . Evidence of the user&#39;s preference is largely gathered by implicit evidence of items that are liked or disliked by monitoring the user&#39;s activity. A positive vote is given to an item that the user dwells over for a threshold period of time, while negative votes are given to items that are briefly reviewed and jumped away from for review of other content. Furthermore, a user via the user interface  22  may select an item for a positive or negative vote. For example, in a first iteration a user may be presented with a list of reviewed items for a selected time period. A user may then select amongst a variety of filtering components residing in a filtering system  20 . Additionally, the user may select or provide positive or negative votes for items. The modified or filtered reviewed item list may then be inputted into the collaborative filtering system  18  as further evidence of user preferences. The collaborative filtering system  18  can then provide a recommendation list of items ranked by preference that a user may be interested in based on all preferences inputted into the collaborative filtering system  18 . 
     The collaborative filtering system  18  ranks selections based on viewing habits of users of the system  10  in addition to being able to consider time segments or intervals of those viewing habits. The collaborative filtering system  18  considers the temporal history of the content viewed by users of the system (e.g., a multi-user system) and dates the list of available information  28  (e.g., available media) that will be made available by an information delivery system  26 . The collaborative filtering system  18  scores active user events for every title stored in the database  12 . The collaborative filtering system  18  can also only consider those titles that are relevant. For example, if the user asks for recommendations about titles currently viewable, then only those titles need be considered. The collaborative filtering system  18  then provides a reviewed items list and a ranked selectable information list based on a requested time period to the user interface  22  via the filtering system  20 . The ranked selectable information list contains recommendations of selectable titles within the time period requested from a particular time segment via the user interface  22 . The collaborative filtering system  18  provides the ranked set of titles that may be of interest to a system user based on similar titles that the users of the system have historically viewed in that time segment. The filtering system  20  includes various filters for adding, deleting or modifying the reviewed items list (e.g., assigning positive or negative votes to items) and inputting the new evidence of preferences into the collaborative filtering system  18  for updating of the reviewed items list and the ranked selectable information list. For example, a popularity filter may be employed to provide shows of interest that users of the system are not likely to know about. Furthermore, text descriptions of items may be added to the reviewed item list employing a search engine or the like. The new preferences may be inputted into the collaborative filtering system  18  and a new recommendation list generated. 
     The user interface  22  is provided with functionality to allow a user to select between the ranked selectable information set. Once a selection of a title to view is made by a user via the user interface  22 , a request is transmitted to the information delivery system  26 , which transmits the requested information corresponding to the selected title to a viewer  24 . The viewed information is then transmitted back to the previously viewed information database  12  for logging and for providing the viewed title and time segment information to the collaborative filtering system  18 . The viewed information may be employed in additional training modes to ensure that the collaborative filtering system  18  is continuously and automatically updated with the latest viewing information. Alternatively, training can be employed remotely and the collaborative filtering system  18  periodically updated. If the ranked selectable information set is not adequate for a particular time segment covering the time period requested, the collaborative filtering system  18  automatically broadens the particular time segment into additional time segments or autobroadens the ranked selectable information set. Alternatively, the ranked selectable information set can be autobroadened by the filtering system  20 . 
       FIG. 2  illustrates an example of a system  30  utilizing a collaborative filtering system  38  according to another aspect of the present invention. In a training mode, a plurality of time interval models  39  in the collaborative filtering system  38  are trained utilizing selections of previously viewed information over a respective time interval that is logged in a user&#39;s database  32  and selections of previously viewed information that is logged in databases  36  of other shared systems over the same respective time interval. Each time interval model  39  of the collaborative filtering system  38  is trained by examining selection reviewed by a large pool of users for more than a threshold amount of time within a corresponding time interval. Negative votes are also considered by monitoring selections jumped away from after being reviewed briefly within the corresponding time interval. A reviewed item list and a recommendation list for a particular time period is provided by the particular time interval model  39  associated with a time interval covering the time period requested. 
     Title selections are time stamped or tagged with different time segments by event type (e.g., title). The log data is annotated with distinctions about the time of the day that a system user has viewed the information. The time stamps and tags are employed in both the training mode for the time interval of the selection and in the real-time mode for providing reviewed items within a time period requested. Various collaborative filtering techniques may be employed to carry out the present collaborative filtering system (e.g., correlation techniques, Bayesian machine learning, probability tree networks, etc.). 
     In real-time, a target time period is identified by a user interface  42  and appropriate information or evidence about a user&#39;s preferences is inputted into a time interval model  39  associated with the requested time period. Evidences of preferences include information from previously selected information from the database  32  within the time interval, other attributes of the shared system  34  and explicit input of samples of preferred items by the user through the user interface  42 . Evidence of the user&#39;s preferences is largely gathered by implicit evidence of items that are liked or disliked by monitoring the user&#39;s activity. A positive vote is given to an item that the user dwells over for a threshold period of time, while negative votes are given to items that are briefly reviewed and jumped away from for review of other content. Furthermore, a user via the user interface  42  may select an item for a positive or negative vote. A user may also select amongst a variety of filtering components residing in a filtering system  40 . The collaborative filtering system  38  can then provide a recommendation list of items ranked by preference that a user may be interested within a requested time period based on all preferences inputted into the respective time interval model  39  covering the requested time period. 
     The respective time interval model  39  of the collaborative filtering system  38  then provides a reviewed items list and a ranked selectable information list based on a requested time period within the time interval to the user interface  42  via the filtering system  40 . New preferences may be inputted into a respective time interval model  39  of the collaborative filtering system  38  and a new recommendation list generated. The user interface  42  is provided with functionality to allow a user to select between the ranked selectable information set. Once a selection of a title to review is made by a user via the user interface  42 , a request is transmitted to the information delivery system  46 , which transmits the requested information corresponding to the selected title to a viewer  44 . The viewed information is then transmitted back to the previously viewed information database  32  for logging and for providing the viewed title and time segment information to the collaborative filtering system  38 . The viewed information may be employed in additional training modes to ensure that the time interval models of the collaborative filtering system  38  are continuously and automatically updated with the latest viewing information. 
       FIG. 3  illustrates an example of a system  50  utilizing a collaborative filtering system  58  according to yet another aspect of the present invention. The collaborative filtering system  58  employs a single collaborative filter model in which previously viewed information is provided with a time stamp according to event occurrence for each time interval or segment. In other words, a title being selected within two different time intervals is treated as different event occurrences, such that training is performed as if the event occurrence has occurred with a totally different title. Again, various collaborative filtering techniques may be employed to carry out the collaborative filtering system  58 . 
     In a training mode, the collaborative filtering system  58  utilizes selections of previously viewed information that is logged in a user&#39;s database  52  and selections of previously viewed information that is logged in databases  56  of other shared systems by the item and the time the item was reviewed for some threshold amount of time by users. Additionally, negative votes can be defined as items that a user has jumped away from after a minimal amount of review time. It is to be appreciated that the database information may be stored locally at the system  50 , remotely at a remote server or the like (e.g., in a single database) or across a number of servers. Additionally, the collaborative filtering system  58  may reside locally at the system  50 , remotely at a remote server or across a number of servers. The collaborative filtering system  58  is trained by examining selection reviewed by a large pool of users for more than a threshold amount of time by the item and the time the item was reviewed. Temporal filtering can then be employed to provide a reviewed item list and a recommendation list for a particular time segment or interval. Title selections are time stamped with different time segments by item or event type (e.g., title) and the time the event was reviewed. The log data is annotated with distinctions about the time of the day that a system user has viewed the information. Various collaborative filtering techniques may be employed to carry out the present collaborative filtering model (e.g., correlation techniques, Bayesian machine learning, probability tree networks, etc.). 
     In real-time, a target time period is identified by a user interface  62  and appropriate information or evidence about a user&#39;s preferences is inputted into the collaborative filtering system  58 . Evidences of preferences include information from previously selected information from the database  52 , other attributes of the shared system  54  and explicit input of samples of preferred items by the user through the user interface  62 . Evidence of the user&#39;s preference is largely gathered by implicit evidence of items that are liked or disliked by monitoring the user&#39;s activity. Furthermore, a user via the user interface  62  may select an item for a positive or negative vote. The collaborative filtering system  58  can then provide a recommendation list of items ranked by preference that a user may be interested in based on all preferences inputted into the collaborative filtering system  58 . 
     The collaborative filtering system  58  ranks selections based on viewing habits of users of the system  60  in addition to being able to consider time segments or intervals of those viewing habits. The collaborative filtering system  58  considers the temporal history of the content viewed by users of the system (e.g., a multi-user system) and dates the list of available information  68  (e.g., available media) that will be made available by an information delivery system  66 . The collaborative filtering system  58  then provides a reviewed items list and a ranked selectable information list based on a requested time period to the user interface  62  via the filtering system  60 . The ranked selectable information list contains recommendations of selectable titles within the time period requested from a particular time segment via the user interface  62 . The collaborative filtering system  58  provides the ranked set of titles that may be of interest to a system user based on similar titles that the users of the system have historically viewed in that time segment. The filtering system  60  includes various filters for adding, deleting or modifying the reviewed items list (e.g., assigning positive or negative votes to items) and inputting the new evidence of preferences into the collaborative filtering system  68  for updating of the reviewed items list and the ranked selectable information list. The new preferences may be inputted into the collaborative filtering system  58  and a new recommendation list generated. 
     The user interface  62  is provided with functionality to allow a user to select between the ranked selectable information set. Once a selection of a title to view is made by a user via the user interface  62 , a request is transmitted to the information delivery system  66 , which transmits the requested information corresponding to the selected title to a viewer  64 . The viewed information is then transmitted back to the previously viewed information database  52  for logging and for providing the viewed title and time segment information to the collaborative filtering system  58 . The viewed information may be employed in additional training modes to ensure that the collaborative filtering system  58  is continuously and automatically updated with the latest viewing information. Alternatively, training can be employed remotely and the collaborative filtering system  58  periodically updated. 
     It is to be appreciated that the present invention is particularly suited for providing selected titles of programs or other multimedia utilizing an electronic programming guide. In this application, multiple members or users in a family utilize a single television system making it improbable to determine preferences for any particular member of the family. Therefore, the present system and method are ideally suited for this application because household viewing habits are typically consistent at different times of a day for weekdays versus weekends verse holidays. For example, children may watch children shows early Saturday mornings, while adults may watch different types of shows at different time periods, for example, soap operas at midafternoon, sports on weekends, news at night and more adult oriented shows late at night. The collaborative filtering models of the present invention apply time segmenting to an electronic programming guide for providing recommendations of program selections based on the temporal history of media viewing habits in a household. 
       FIG. 4  illustrates an example of an electronic programming guide system  70  utilizing a collaborative filtering system  78  employing time based collaborative filtering techniques. In a training mode, the collaborative filtering system  78  utilizes selections of previously viewed information that is logged in a user&#39;s database  72  and selections of previously viewed information that is logged in databases  76  of other shared systems. The collaborative filtering system  78  is trained by examining selections reviewed by a large pool of users for more than a threshold amount of time. Negative votes are also considered by monitoring selections jumped away from after being reviewed briefly. The collaborative filtering system can be trained based on any of the techniques illustrated in  FIGS. 1-3 . 
     The collaborative filtering system  78  ranks selections based on viewing habits of users of the system  70  in addition to considering time segments or intervals of those viewing habits. Various collaborative filtering techniques may be employed to carry out the present collaborative filtering model application with respect to an electronic programming guide. In real-time, a target time period is identified by a user interface  82  and appropriate information or evidence about a user&#39;s preferences is inputted into the collaborative filtering system  78 . The collaborative filtering system  78  considers the temporal history of the programs viewed by users of the system (e.g., family members of a household) and dates and times of electronic program guide data  88  of available programs that can be made available by a program delivery system  86 . The collaborative filtering system  78  is operable to receive other attributes  74  of the shared system (e.g., family profile information by family member, family profile information by family viewing time intervals). Evidence of preferences can also include explicit input of samples of preferred items by the user through the user interface  82 . 
     Evidence of the user&#39;s preference is largely gathered by implicit evidence of shows that are liked or disliked by monitoring the user&#39;s viewing activity. A positive vote is given to a program that the user watches for a threshold period of time, while negative votes are given to shows that are briefly watched and jumped away from for review of other shows. Furthermore, a user via the user interface  82  may select an item for a positive or negative vote. For example, in a first iteration a user may be presented with a list of reviewed programs for a selected time period. A user may then select amongst a variety of filtering components residing in a filtering system  80 . Additionally, the user may select or provide positive or negative votes for items. The modified or filtered reviewed item list may then be inputted into the collaborative filtering system  78  as further evidence of user preferences. The collaborative filtering system  78  can then provide a recommendation list of programs ranked by preference that a user may be interested in based on all preferences inputted into the collaborative filtering system  78 . 
     The collaborative filtering system  78  ranks selections based on viewing habits of users of the system  70  in addition to being able to consider time segments or intervals of those viewing habits. The collaborative filtering system  78  and/or temporal filters are employed to consider the temporal history of the shows viewed by users of the system (e.g., a multi-user system) and dates the list of available information  88  (e.g., available media) that will be made available by an information delivery system  86 . The collaborative filtering system  78  then provides a reviewed items list and a ranked selectable information list based on a requested time period to the user interface  82  via the filtering system  80 . The ranked selectable information list contains recommendations of selectable programs within the time period requested from a particular time segment via the user interface  82 . The collaborative filtering system  78  provides the ranked set of programs that may be of interest to a system user based on similar programs that the users of the system have historically watched in that time segment. 
     The filtering system  80  includes various filters for adding, deleting or modifying the reviewed items list (e.g., assigning positive or negative votes to items) and inputting the new evidence of preferences into the collaborative filtering system  78  for updating of the reviewed items list and the ranked selectable information list. For example, a popularity filter may be employed to provide shows of interest that users of the system are not likely to know about. Furthermore, text descriptions of items may be added to the reviewed item list employing a search engine or the like. The new preferences may be inputted into the collaborative filtering system  78  and a new recommendation list generated. 
     The collaborative filtering system  78  receives titles of previously viewed programs that are logged in a database  72 . Each title is time stamped with different time segments and the percentage of the complete program that the user has watched the program is logged in the database  72 . The log data is annotated with distinctions about the time of the day that a system user has watched the television program. As previously mentioned, a day can be segmented into early morning, mid-morning, afternoon, late afternoon, early evening, evening, late night. Additional segments can include information that is viewed on weekdays verse weekends. Furthermore, non-weekdays can be further subdivided into Saturday, Sunday and holidays. Elements from a dataset are considered tagged with these time segments or intervals and trained with this data employing conventional collaborative filtering techniques. 
     The output provided to the user at the user interface  82  is a personalized program guide for upcoming broadcasted media (e.g., a view of the content in the next hour or next several hours that is sorted by inferred user preferences). A filtering system  80  is coupled to the user interface  82 . The filtering system  80  receives a reviewed program list from the collaborative filtering system  78 , directly from the previously viewed programs database  72  or some other mechanism coupled to the previously viewed programs database  72 . The filtering system  80  provides the functionality associated with various filters that can be invoked through the user interface component  82 . Once a selection of a title to view is made by a user via the user interface  82 , a request is transmitted to the information delivery system  86  by the filtering system  80 . The program delivery system  86  transmits the requested program corresponding to the selected title to a viewer  84 . Information about the viewed program is then transmitted back to the previously viewed programs database  72  for logging and for providing the viewed information to the collaborative filtering system  78  for further training of the collaborative filtering system  78 . Therefore, the collaborative filtering system  78  is continuously and automatically being updated with the latest viewed programs. If the reviewed items list and the recommended items list are not adequate for a particular time segment covering the time period requested, the collaborative filtering system  78  automatically broadens the particular time segment into additional time segments. Alternatively, the reviewed items list and the recommended items list can be autobroadened by the filtering system  80  via the user interface  82 . 
       FIG. 5  illustrates a block diagram of possible components associated with the filtering system  80 . The filtering system  80  comprises a live show selection filter  90 . The live show selection filter  90  provides recommendations for live programs already in progress that a user may wish to catch at any given time period. The live show selections are programs that are currently in progress that are being recorded by the system based on determined preferences of the users of the system, and are only limited by the amount of available recording space and available tuners. A time period filter  92  is a temporal filter that filters out the recommended shows by time period based on a time period requested. The time period filter  92  can also be adapted to provide the functionality of the live show selections filter  90  if a selection of “now” is selected. The recommended show selections provided by the time period filter include ranked shows based on selections that a user is most likely to watch at a future time period predicted by utilizing the shared system history for the time interval covering the requested time period. 
     A popularity filter  94  is also provided. The popularity filter employs popularity techniques by scoring selection based on the probability that the user would not know about the selection. For example, a popularity of a selection may be evaluated by multiplying a collaborative filter score of a selection by one minus the probability that the user may know of the selection or by multiplying the collaborative filter score by the probability that the user does not know of the selection. A patterns search engine  96  is also provided so that text and genre can be inputted into the system  70  as evidence of preferences to the collaborative filtering system  78 . The filtering system  80  also includes an adding items of interest and update component  98 . The adding items of interest and update component  98  allows a user to edit, modify and update a reviewed selections list, so that the modified list can be input into the collaborative filtering system  78 . 
       FIG. 6  illustrates an example of a possible user interface  110  that allows a user to review available selections that a user is interested in quickly without unduly searching, for example, via a remote control. A day scroll down menu  112  and a time scroll down menu  114  are provided to allow a user to select a start time for providing recommendations of program selections to the user. A period scroll down menu  116  is also provided that allows a user to select a time period for providing suggested program selections. The period scroll down menu  116  illustrates a few possible selections that a user can select, such as “anytime”, “now” (e.g., live selections) or “next two hours”. Other selections may be provided, such as “next 12 hours”, “next day”, “next week”, “2 hours from now”, etc. Once a user has selected a date and time to start from and a time period, the user can then select an “enter/update” button  128 . A plurality of recommended selections is then provided to the user in a “recommended items of interest” window  126 . The selections fall within the time period requested, and are ranked according to selections that the user is most likely to be interested in viewing (e.g., inferred strength) based on employing collaborative filtering techniques on historical data for a time interval encompassing the requested time period. 
     A second window  124  is provided that includes a “previously viewed items” list. The “previously viewed items” list includes a plurality of items that have been reviewed by users of the system. Items that the user has reviewed for a threshold period of time within the time period selected are assigned a positive vote as indicated by a check mark in a box located in a positive vote column. Items that the user has jumped away from after being reviewed briefly within a time period selected are assigned a negative vote as indicated by a check mark in a box located in a negative vote column. The “previously viewed items” is input into the collaborative filtering system to provide the recommended items in the window  126 . The “previously viewed items” is editable such that the user can edit or assign positive and negative votes to an item and select the “enter/update” button  128  to modify the recommended items of interest in the window  126 . 
     A population filter selection slide  118  is provided to allow a user to adjust a popularity filter. The popularity filter evaluates selections that are likely to be of interest to a user that the user is most likely not to know about. For example, a selection can be assigned a value by multiplying the collaborative filter value by the probability that the user did not know about the selection. If the population selection box is selected, a plurality of selections that a user does not know about, but may find of interest, is provided in the recommended items of interest window  126 . Additionally, a first search window  120  is provided for allowing a user to enter patterns of type or text and/or conduct type or text searches for updating the evidence about the kind of show that the user is interested in reviewing. A second search window  122  is provided for displaying the results of the pattern search. A user may select to view a show of interest by selecting a show in the “recommended items of interest” window  126  and selecting the “select show” button  129 . 
     It is to be appreciated that the electronic programming guide system of the present invention may reside on a television set top box. Alternatively, portions of the electronic programming guide system may reside on a television set top box and portions of the electronic programming guide system may reside on a remote server system. A remote server system is further suited for applications in which general recommendations are made to customer&#39;s forming different clusters. For example, different cluster groups can be determined by analyzing the historical viewing of a plurality of customers. A set of general recommendations can then be determined for each cluster by utilizing the collaborative filtering techniques of the present invention. A general set of recommendations can then be provided to each user of a cluster based on viewing habits within particular time intervals and available shows within the time interval. 
       FIG. 7  illustrates a program delivery system  142  that resides on a server  140  coupled to a set top box  160  of a client via a communication link  150 . The program delivery system  142  includes a global inference system  144  and a cluster specific inference system  146 . The cluster specific inference system  146  reviews specific viewing history of a plurality of users and determines user preferences based on correlations between groups of various users. The global inference system  144  reviews specific viewing history of all or a sample of users for providing preferences based on shows that are the most popular. Regardless of the inference system being employed, the server or servers  140  transmit a set of recommendations to the set top box  160 . Additional cluster specific inference systems can be provided for members of various clusters coupled to the server  140 . The set top box  160  includes an electronic programming guide system  161  comprised of a user interface component  162  coupled to a filtering system  164 . The set top box  160  also includes volatile memory storage  166  for catching live shows of interest. The server  140  and the program delivery system  142  are operable to understand both the electronic programming guide system  161  and individual subscriber viewing habits based on selections made at the set top box  160 . These selections can be determined by requests made at the set top box  160  or alternatively by transmitting information from the set top box  160  back to the server  140 . This information is logged and used to train the global and the cluster specific inference systems  144  and  146 . The global and specific inference systems  144  and  146  are further operable to provide a set of recommendations to a user of the set top box  160  based on viewing habits within specific time intervals of the set top box  160 . Providing the functionality of logging, training and recommending at the server eliminates the need for applications at the set top box allowing set top boxes with limited resources to be implemented at the client end. The filtering system  164  gives the user the ability to add, delete and modify results transmitted to the set box  160 . 
     The global inference system  144  is operable to group client users into clusters based on all users viewing habits within time intervals employing the electronic programming guide system of the present invention. The global inference system  144  then utilizes this information to pre-generate a different recommendation set for each cluster of users. The recommendations do not necessarily rely on real time data, but can employ historical data periodically to determine a recommendation set. The global inference system  144  and the cluster specific inference system  146  can reside across one or more servers and/or be downloaded periodically to each individual set top box coupled to the server(s)  140 . 
     In view of the foregoing structural and functional features described above, methodologies in accordance with various aspects of the present invention will be better appreciated with reference to  FIGS. 8-10 . While, for purposes of simplicity of explanation, the methodology of  FIGS. 8-10  is shown and described as executing serially, it is to be understood and appreciated that the present invention is not limited by the illustrated order, as some aspects could, in accordance with the present invention, occur in different orders and/or concurrently with other aspects from that shown and described herein. Moreover, not all illustrated features may be required to implement a methodology in accordance with an aspect the present invention. It is further to be appreciated that the following methodology may be implemented as computer-executable instructions, such as software stored in a computer-readable medium. Alternatively, the methodology may be implemented as hardware or a combination of hardware and software. 
       FIG. 8  illustrates one particular methodology for training and downloading a collaborative filtering system employing the temporal history of a plurality of systems. The methodology begins at  180  where retrieved content of a plurality of systems is monitored based on the time that the content is reviewed. Information that is retrieved and reviewed for a threshold period of time is logged and time stamped with the title in one or more databases at  190 . Information that is retrieved and reviewed for a brief period of time and then jumped away from after a brief review is logged and time stamped with the title in the one or more databases at  200 . The methodology then proceeds to  210  to train a collaborative filtering system. The collaborative filtering system is trained with positive votes assigned to titles reviewed for a threshold period of time and negative votes assigned to titles jumped away from after a brief review. At  220 , the trained collaborative system is downloaded to the individual systems. The downloading can be done locally or from a remote link via a network. 
       FIG. 9  illustrates one particular methodology for providing a general recommendation set employing the trained collaboration filter system of the present invention. The methodology begins at  250  where a requested time period is provided to the collaborative filtering system from, for example, a user interface or the like. At  260 , a reviewed item list is provided with items within the requested time period having indications of a positive or a negative vote associated with one or more items. Filtering may be provided to the reviewed item list at  270 . For example, a user may selectively filter the reviewed item list via a user interface employing one or more filters to the reviewed item list. The collaborative filtering system then automatically updates the reviewed item list providing the selected filters and any additional attributes of the users of the system at  280 . At  290 , the collaborative filtering system provides recommendations of titles in ranked order for the requested time period after the reviewed list has been filtered with the one or more filters in addition to the information provided about additional attributes of users of the system. 
       FIG. 10  illustrates one particular methodology for providing a recommendation set by training and utilizing cluster specific collaboration filter systems according to one particular aspect of the present invention. The methodology begins at  300  where information retrieved by all of the systems is monitored. Titles of retrieved information are then time stamped and assigned a value based on the number of times the title has been retrieved by a system for each system at  310 . At  320 , system cluster groups are evaluated by determining correlations between systems utilizing the retrieved information habits of each system. At  330 , cluster specific collaborative filtering systems are trained. At  340 , sets of ranked titles are provided for each cluster by a cluster specific collaborative filtering system based on particular preferences of each cluster. The individual ranked titles are updated at the system utilizing system specific filters at  350 . 
     With reference to  FIG. 11 , an exemplary system for implementing the invention includes a conventional personal or server computer  420 , including a processing unit  421 , a system memory  422 , and a system bus  423  that couples various system components including the system memory to the processing unit  421 . The processing unit may be any of various commercially available processors, including Intel x86, Pentium and compatible microprocessors from Intel and others, including Cyrix, AMD and Nexgen; Alpha from Digital; MIPS from MIPS Technology, NEC, IDT, Siemens, and others; and the PowerPC from IBM and Motorola. Dual microprocessors and other multi-processor architectures also can be used as the processing unit  421 . 
     The system bus may be any of several types of bus structure including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of conventional bus architectures such as PCI, VESA, Microchannel, ISA and EISA, to name a few. The system memory includes read only memory (ROM)  424  and random access memory (RAM)  425 . A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within the computer  420 , such as during start-up, is stored in ROM  424 . 
     The computer  420  further includes a hard disk drive  427 , a magnetic disk drive  428 , e.g., to read from or write to a removable disk  429 , and an optical disk drive  430 , e.g., for reading a CD-ROM disk  431  or to read from or write to other optical media. The hard disk drive  427 , magnetic disk drive  428 , and optical disk drive  430  are connected to the system bus  423  by a hard disk drive interface  432 , a magnetic disk drive interface  433 , and an optical drive interface  434 , respectively. The drives and their associated computer-readable media provide nonvolatile storage of data, data structures, computer-executable instructions, etc. for the server computer  420 . Although the description of computer-readable media above refers to a hard disk, a removable magnetic disk and a CD, it should be appreciated by those skilled in the art that other types of media which are readable by a computer, such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, and the like, may also be used in the exemplary operating environment. 
     A number of program modules may be stored in the drives and RAM  425 , including an operating system  435 , one or more application programs  436 , other program modules  437 , and program data  438 . A user may enter commands and information into the computer  420  through a keyboard  440  and pointing device, such as a mouse  442 . Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  421  through a serial port interface  446  that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port or a universal serial bus (USB). A monitor  447  or other type of display device is also connected to the system bus  423  via an interface, such as a video adapter  448 . In addition to the monitor, computers typically include other peripheral output devices (not shown), such as speakers and printers. 
     The computer  420  may operate in a networked environment using logical connections to one or more remote computers, such as a remote server or client computer  449 . The remote computer  449  may be a workstation, a server computer, a router, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  420 , although only a memory storage device  450  has been illustrated in  FIG. 11 . The logical connections depicted in  FIG. 11  include a local area network (LAN)  451  and a wide area network (WAN)  452 . Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN networking environment, the computer  420  is connected to the local network  451  through a network interface or adapter  453 . When used in a WAN networking environment, the server computer  420  typically includes a modem  454 , or is connected to a communications server on the LAN, or has other means for establishing communications over the wide area network  452 , such as the Internet. The modem  454 , which may be internal or external, is connected to the system bus  423  via the serial port interface  446 . In a networked environment, program modules depicted relative to the computer  420 , or portions thereof, may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     In accordance with practices of persons skilled in the art of computer programming, the present invention is described below with reference to acts and symbolic representations of operations that are performed by the computer  420 , unless indicated otherwise. Such acts and operations are sometimes referred to as being computer-executed. It will be appreciated that the acts and symbolically represented operations include the manipulation by the processing unit  421  of electrical signals representing data bits which causes a resulting transformation or reduction of the electrical signal representation, and the maintenance of data bits at memory locations in the memory system (including the system memory  422 , hard drive  427 , floppy disks  429 , and CD-ROM  431 ) to thereby reconfigure or otherwise alter the computer system&#39;s operation, as well as other processing of signals. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, or optical properties corresponding to the data bits. 
     The present invention has been illustrated with respect to a programming methodology and/or computer architecture and a particular example, however, it is to be appreciated that various programming methodology and/or computer architecture suitable for carrying out the present invention may be employed and are intended to fall within the scope of the hereto appended claims. 
     The invention has been described with reference to the preferred aspects of the invention. Obviously, modifications and alterations will occur to others upon reading and understanding the foregone detailed description. It is intended that the invention be construed as including all such modifications alterations, and equivalents thereof.