Matching bias and relevancy in reviews with artificial intelligence

Matching bias and relevancy in online reviews is provided. A review from an internet media source is gathered and parsed to identify a number of entities with the review. A number of internet media posts are parsed to identify entities within the posts. Entities in the review are mapped to entities in the internet media posts. A bias and context are determined for the review. A bias and context are also determined for a user reading the review. A relevancy score of the review is determined by comparing the bias and context of the review to the bias and context of the user, and the review is displayed among a number of reviews according to its relevancy score for the user.

BACKGROUND

The disclosure relates generally to reviews posted on the internet and more specifically to determining a bias and relevancy of a review and matching it to a bias and context of a user reading the review.

Informal word of mouth communications between parties has long been part of human behavior concerning the evaluation of goods and services. With the introduction of online reviews, word of mouth can now be systematically accessed in an organized way on a global scale without exclusive reliance on scattered reviews from a person's immediate social surroundings. Not only do retailers organize and provide reviews for customers and readers, third party online review websites allow users to provide and read reviews for a myriad of goods and services. However, online reviews are subject to bias.

The relevancy of online reviews for a reader varies with the bias and context of both the reviewer and the reader of the review. What is relevant for one reader might not be relevant for another. Online reviews might be motivated by factors other than quality of a given service or product and might not even be a result of an experience with a given service or product. Extraneous variables might exert a significant influence upon a reviewer as well as the reader.

SUMMARY

An illustrative embodiment provides a computer-implemented method for matching bias and relevancy in online reviews is provided. The method comprises gathering a review from an internet media source and parsing it to identify a number of entities with the review. A number of internet media posts are parsed to identify entities within the posts. Entities in the review are mapped to entities in the internet media posts. A bias and context are determined for the review. A bias and context are also determined for a user reading the review. A relevancy score of the review is determined by comparing the bias and context of the review to the bias and context of the user, and the review is displayed among a number of reviews according to its relevancy score for the user.

Another illustrative embodiment provides a system for bias matching. The system comprises a bus system; a storage device connected to the bus system, wherein the storage device stores program instructions; and a number of processors connected to the bus system, wherein the number of processors execute the program instructions to: gather a review from an internet media source; parse the review to identify a number of entities within the review; parse a number of internet media posts to identify entities within the posts; map entities in the review to entities in the internet media posts; determine a bias and context of the review; determine a bias and context for a user reading the review; determining a relevancy score of the review for the user by comparing the bias and context of the review to the bias and context of the user; and display the review among a number of reviews according to its relevancy score for the user.

Another illustrative embodiment provides a computer program product for bias matching. The computer program product comprises a non-volatile computer readable storage medium having program instructions embodied therewith, the program instructions executable by a number of processors to cause the computer to perform the steps of: gathering a review from an internet media source; parsing the review to identify a number of entities within the review; parsing a number of internet media posts to identify entities within the posts; mapping entities in the review to entities in the internet media posts; determining a bias and context of the review; determining a bias and context for a user reading the review; determining a relevancy score of the review for the user by comparing the bias and context of the review to the bias and context of the user; and displaying the review among a number of reviews according to its relevancy score for the user.

DETAILED DESCRIPTION

Illustrative embodiments recognize and take into account that the relevancy of online reviews varies with the context and viewpoint of both the reviewer and a reader of the review. Illustrative embodiments provide the technical solution of determining the relevance of reviews for specific readers of the reviews.

Illustrative embodiments recognize and take into account that online reviews might be motivated by factors other than quality of a given service or product and that extraneous factors might exert a significant influence upon a reviewer including a mistaken presumption on the part of the reviewer. The illustrative embodiments provide the technical solution of enriching reviews to store underlying context in which user has given the review and use it to match bias and context of the reviewer with that of a reader.

Illustrative embodiments also recognize and take into account that the relevance of a review for a specific reader might change for the reader under different circumstances. For example, a restaurant review might be relevant for a reader looking for a venue for an office party but might be irrelevant a week later when the same reader is looking for a venue for a birthday party. Therefore, illustrative embodiments provide the technical solution of matching the context of the review to the real-time context of the reader.

Illustrative embodiments also recognize and take into account that bias in reviews is a product of the point-of-view of the reader. Illustrative embodiments provide the technical solution of addressing the perception of bias by surfacing reviews written by reviewers with similar mindset and context as the reader. The readers can also manually adjust the match in mindset they would like in the reviews surfaced to them.

Illustrative embodiments provide a method of enriching the reviews written by a reviewer with additional context at the time of writing. This context can be mapped to the context of the user reading the reviews at the time of reading to surface the most relevant reviews. A review written by the same person may vary depending on the context.

By creating profiles of users and similar users, analyzing the mindset/predispositions/bias of users based on those profiles over a period of time and specifically at the time and context while providing the review, the illustrative embodiments determine the relevancy of a review and store the review with the entities, bias of the reviewer, and context. Illustrative embodiments merge events in news media with those predispositions/biases to offer deeper analysis of the context that motivated the reviewer while reviewing specific entities.

FIG. 1depicts a pictorial representation of a network of data processing systems in which illustrative embodiments can be implemented. Network data processing system100is a network of computers, data processing systems, and other devices in which the illustrative embodiments may be implemented. Network data processing system100contains network102, which is the medium used to provide communications links between the computers, data processing systems, and other devices connected together within network data processing system100. Network102may include connections, such as, for example, wire communication links, wireless communication links, and fiber optic cables.

In the depicted example, server104and server106connect to network102, along with storage108. Server104and server106may be, for example, server computers with high-speed connections to network102. In addition, server104and server106may provide a set of one or more connector services for managing idempotent operations on a system of record, such as storage108. An idempotent operation is an identical operation, which was previously performed or executed, that has the same effect as performing a single operation. Also, it should be noted that server104and server106may each represent a plurality of servers providing management of idempotent operations for a plurality of system of records.

Client110, client112, and client114also connect to network102. Clients110,112, and114are clients of server104and server106. Server104and server106may provide information, such as boot files, operating system images, and software applications to clients110,112, and114.

In this example, clients110,112, and114are shown as desktop or personal computers. However, it should be noted that clients110,112, and114are intended as examples only. In other words, clients110,112, and114may include other types of data processing systems, such as, for example, network computers, laptop computers, tablet computers, handheld computers, smart phones, smart watches, personal digital assistants, gaming devices, set-top boxes, kiosks, and the like. Users of clients110,112, and114may utilize clients110,112, and114to access system of records corresponding to one or more enterprises, via the connector services provided by server104and server106, to perform different data operations. The operations may be, for example, retrieve data, update data, delete data, store data, and the like, on the system of records.

Storage108is a network storage device capable of storing any type of data in a structured format or an unstructured format. In addition, storage108may represent a plurality of network storage devices. Further, storage108may represent a system of record, which is an authoritative data source, corresponding to an enterprise, organization, institution, agency, or similar entity. Furthermore, storage unit108may store other types of data, such as authentication or credential data that may include user names, passwords, and biometric data associated with client users and system administrators, for example.

In addition, it should be noted that network data processing system100may include any number of additional servers, clients, storage devices, and other devices not shown. Program code located in network data processing system100may be stored on a computer readable storage medium and downloaded to a computer or other data processing device for use. For example, program code may be stored on a computer readable storage medium on server104and downloaded to client110over network102for use on client110.

In the depicted example, network data processing system100may be implemented as a number of different types of communication networks, such as, for example, an internet, an intranet, a local area network (LAN), and a wide area network (WAN).FIG. 1is intended as an example only, and not as an architectural limitation for the different illustrative embodiments.

FIG. 2illustrates a block diagram of a system for matching bias and relevancy of reviews in accordance with an illustrative embodiment. Computer system200is connected to user profile260, external sources270, and devices290. User profile260comprise bias/preference262, context264, history266, and manual filter set by the user268.

Machine intelligence218can be implemented using one or more systems such as an artificial intelligence system, a neural network, a Bayesian network, an expert system, a fuzzy logic system, a genetic algorithm, or other suitable types of systems. Machine learning220and predictive algorithms222may make computer system200a special purpose computer for dynamic predictive modelling of bias and relevancy of online reviews.

In an embodiment, processing unit216comprises one or more conventional general purpose central processing units (CPUs). In an alternate embodiment, processing unit216comprises one or more graphical processing units (GPUs). Though originally designed to accelerate the creation of images with millions of pixels whose frames need to be continually recalculated to display output in less than a second, GPUs are particularly well suited to machine learning. Their specialized parallel processing architecture allows them to perform many more floating point operations per second then a CPU, on the order of 100× more. GPUs can be clustered together to run neural networks comprising hundreds of millions of connection nodes.

Modeling program230comprises information gathering250, parsing232, modeling234, comparing236, and displaying238. Information gathering252is configured to gather data from personal profile260and external sources270.

Thus, processing unit216, machine intelligence218, and modeling program230transform a computer system into a special purpose computer system as compared to currently available general computer systems that do not have a means to perform machine learning predictive modeling such as computer system200ofFIG. 2. Currently used general computer systems do not have a means to accurately model bias and relevancy of online reviews according the bias and context of a user reading those reviews.

There are three main categories of machine learning: supervised, unsupervised, and reinforcement learning. Supervised machine learning comprises providing the machine with training data and the correct output value of the data. During supervised learning the values for the output are provided along with the training data (labeled dataset) for the model building process. The algorithm, through trial and error, deciphers the patterns that exist between the input training data and the known output values to create a model that can reproduce the same underlying rules with new data. Examples of supervised learning algorithms include regression analysis, decision trees, k-nearest neighbors, neural networks, and support vector machines.

If unsupervised learning is used, not all of the variables and data patterns are labeled, forcing the machine to discover hidden patterns and create labels on its own through the use of unsupervised learning algorithms. Unsupervised learning has the advantage of discovering patterns in the data with no need for labeled datasets. Examples of algorithms used in unsupervised machine learning include k-means clustering, association analysis, and descending clustering.

Whereas supervised and unsupervised methods learn from a dataset, reinforcement learning methods learn from interactions with an environment. Algorithms such as Q-learning are used to train the predictive model through interacting with the environment using measurable performance criteria.

FIG. 3is a diagram that illustrates a node in a neural network in which illustrative embodiments can be implemented. Node300combines multiple inputs310from other nodes. Each input310is multiplied by a respective weight320that either amplifies or dampens that input, thereby assigning significance to each input for the task the algorithm is trying to learn. The weighted inputs are collected by a net input function330and then passed through an activation function340to determine the output350. The connections between nodes are called edges. The respective weights of nodes and edges might change as learning proceeds, increasing or decreasing the weight of the respective signals at an edge. A node might only send a signal if the aggregate input signal exceeds a predefined threshold. Pairing adjustable weights with input features is how significance is assigned to those features with regard to how the network classifies and clusters input data.

Neural networks are often aggregated into layers, with different layers performing different kinds of transformations on their respective inputs. A node layer is a row of nodes that turn on or off as input is fed through the network. Signals travel from the first (input) layer to the last (output) layer, passing through any layers in between. Each layer's output acts as the next layer's input.

FIG. 4is a diagram illustrating a neural network in which illustrative embodiments can be implemented. As shown inFIG. 4, the nodes in the neural network400are divided into a layer of visible nodes410and a layer of hidden nodes420. The visible nodes410are those that receive information from the environment (i.e. a set of external training data). Each visible node in layer410takes a low-level feature from an item in the dataset and passes it to the hidden nodes in the next layer420. When a node in the hidden layer420receives an input value x from a visible node in layer410it multiplies x by the weight assigned to that connection (edge) and adds it to a bias b. The result of these two operations is then fed into an activation function which produces the node's output.

In symmetric networks, each node in one layer is connected to every node in the next layer. For example, when node421receives input from all of the visible nodes411-413each x value from the separate nodes is multiplied by its respective weight, and all of the products are summed. The summed products are then added to the hidden layer bias, and the result is passed through the activation function to produce output431. A similar process is repeated at hidden nodes422-424to produce respective outputs432-434. In the case of a deeper neural network, the outputs430of hidden layer420serve as inputs to the next hidden layer.

Training a neural network occurs in two alternating phases. The first phase is the “positive” phase in which the visible nodes' states are clamped to a particular binary state vector sampled from the training set (i.e. the network observes the training data). The second phase is the “negative” phase in which none of the nodes have their state determined by external data, and the network is allowed to run freely (i.e. the network tries to reconstruct the input). In the negative reconstruction phase the activations of the hidden layer420act as the inputs in a backward pass to visible layer410. The activations are multiplied by the same weights that the visible layer inputs were on the forward pass. At each visible node411-413the sum of those products is added to a visible-layer bias. The output of those operations is a reconstruction r (i.e. an approximation of the original input x).

In machine learning, a cost function estimates how the model is performing. It is a measure of how wrong the model is in terms of its ability to estimate the relationship between input x and output y. This is expressed as a difference or distance between the predicted value and the actual value. The cost function (i.e. loss or error) can be estimated by iteratively running the model to compare estimated predictions against known values of y during supervised learning. The objective of a machine learning model, therefore, is to find parameters, weights, or a structure that minimizes the cost function.

Gradient descent is an optimization algorithm that attempts to find a local or global minima of a function, thereby enabling the model to learn the gradient or direction that the model should take in order to reduce errors. As the model iterates, it gradually converges towards a minimum where further tweaks to the parameters produce little or zero changes in the loss. At this point the model has optimized the weights such that they minimize the cost function.

Neural networks can be stacked to create deep networks. After training one neural net, the activities of its hidden nodes can be used as training data for a higher level, thereby allowing stacking of neural networks. Such stacking makes it possible to efficiently train several layers of hidden nodes. Examples of stacked networks include deep belief networks (DBN), deep Boltzmann machines (DBM), convolutional neural networks (CNN), recurrent neural networks (RNN), and spiking neural networks (SNN).

FIG. 5is a process flow for determining bias and relevancy in reviews in accordance with an illustrative embodiment. Process500can be implemented in a neural network, such as neural network400inFIG. 4. Process500begins by gathering a review from an internet media source (step502). The review is then parsed in to identify entities within the review (step504). This parsing can be performed in real-time as soon as the review is submitted. Entities in the review are mapped to unique review IDs to derive a mindset/bias and context of the reviewer.

Process500parses internet media posts such as news stories to identify entities within the posts (step506). The step can be performed using natural language processing (NLP) on news event posts, social media posts, and other online posts that are posted within a specified time frame proximal to the review. The NLP parsing can also identify historical news events and posts related to entities in the review. The news events can come from sources such as, e.g., online and printed formal news outlets or blogs, social medial feeds and posts, and other online media outlets. The event is mapped out based on, but not limited to, what happened, who caused the new event, protagonists and antagonists of the story, where it happened, and bias of the author, publishers, and storyline. Multiple news sources are searched on the same or similar data points to ensure that missed or misrepresented data is captured. Step506also analyzes historical trends of contribution of any author to identify if the author adopts different beliefs over a period of time or is biased from the outset.

Entities in the review are then mapped to the media posts (step508). Discrete structured text, phrases derived from unstructured sources with associated metadata such as author, bias, type of bias, and data are stored in the context in the map instantiated in step502.

From this mapping process500determines if the review has an illogical bias (step510). Described in more detail inFIG. 6below, this step determines if a review has been biased by the recent occurrence of a news event or historical trends in reporting of a topic. If there is an illogical bias in the review, the review is ignored (step512), and process500ends for that review. If no illogical bias is detected, process500keeps the review active (step514).

Process500then determines a bias and context for a user reading the review (step516). This step can employ K-means clustering to determine rules for deriving bias and context of the user from similar users. The step involves determining the mindset and real-time context of the reader comprising factors related to entities of the review. For example, if the user is reading a restaurant the factors might include, without limitation, the type of restaurants the user visits, the food the user orders with respect to time of day, whether the user's food preferences have varied historically based, as well as which factors matter to the user such as value, ambience, health-consciousness, and the relative importance of these factors. Factors can be derived from patterns in mobile data and the footprint of the user related to, e.g., online, mobile, social media, data storage, past orders, posts, feedback, etc. The context of the user can include, without limitation, the time user is considering visiting the restaurant, any special events (e.g., party, wedding anniversary, work functions), and the type of people who might accompany the user (e.g., family, spouse, work colleagues, school friends, etc.).

A relevancy score of the review is determined by comparing the bias and context of the review to the bias and context of the user (step518). This comparison can be made, e.g., by nodes in a layer of neural network400, using approaches such as Euclidean distance between two vectors and other approaches for vector matching. The match score determines the relevancy of the review for the user reading the review. This method predicts bias and adjusts the relevancy of reviews by matching the mindset and context of the reviewer at the time of writing the review with the mindset and context of the user looking to make a decision based on the review. In an embodiment, the use reading the review can adjust the relevancy of reviews based on the user's preferences of weight on bias factor and context to see the reviews according to the user's preference. The adjustment can be made using a manual user filter such as filter268inFIG. 2.

The review is then displayed to the user among other reviews according to its relevancy score for the user (step520). Machine learning can adjust the derivation of bias and context of similar users of reviews as well as weights on bias and context factors for different user clusters based on usage of the reviews and user feedback.

FIG. 6is a process flow for determining illogical bias in a review in accordance with an illustrative embodiment. Process600can be implemented in a neural network, such as neural network400inFIG. 4. Process600is a detailed description of step510inFIG. 5. Process600begins by searching the internet for internet news/media posts within a specific time frame of a review or historically can be linked to the entities in the review (step602). If no news/media posts are found, process600determines that the review was not biased by any contemporary or historical news events and hence there is no illogical bias in the review (step604). For example, if BIASR1of review R1is 0 on a scale of 0 to 1, such a score indicates no illogical bias.

If news/media posts are found related to entities in the review, the algorithm determines if a bias of the review matches a detectable bias in the news (step606). Bias can be detected in media news stories, posts, reviews, etc., through the application of NLP and supervised machine learning with training data. A model can be considered to contain bias if it performs better for comments containing a particular entity term than for comments containing other entity terms. For example, the frequency of negative terms in a particular review (e.g., R1) on entity X can be compared to other reviews that are marked as negative in the training dataset for the same entity X. From this, BIASR1of review R1is calculated.

Similarly, the frequency of negative terms in a news story/post containing the same entity term X from the same specified time frame as review R1can be compared to other new stories/posts in the training dataset marked as negative for the same entity item X. From this comparison, BIASN1of news story N1is determined with respect to entity X. With the above approach, bias scores (in reviews or news) will be between 0 and 1. The closer the score is to 0, the lower the bias. The closer it is to 1, the higher the bias.

In process600, if

BIASR⁢⁢1≠∑in⁢⁢BIASNi/n
where n is the total number of new stories/posts, and if BIASR1is greater than a specified threshold T, R1is considered mismatching/illogically biased and therefore ignored (step608).

Conversely, if

BIASR⁢⁢1≅∑in⁢⁢BIASNi/n
the bias of the review R1does match the bias of the news. Process600therefore determines that the review is more accurate and is applicable (step610).

If no illogical bias is detected in the review, or if the bias in the review matches the bias in the news/media posts, process600keeps the review as active for later consideration in step514ofFIG. 5(step612).

As a general matter, self-motivated reviews exhibit a systematic negative bias in comparison to solicited reviews. Self-motivated reviews can also be influenced by the mere presence of other reviews and tend to display downward temporal trends. Hence self-motivated selection and social influence can bias. Solicited reviews tend to be more positive on average and display greater stability over time. Bias in new media stories/posts can be derived using NLP, comparing key normative words against entities found through parsing.

Turning toFIG. 7, a diagram of a data processing system is depicted in accordance with an illustrative embodiment. Data processing system700is an example of a system in which computer-readable program code or program instructions implementing processes of illustrative embodiments may be run. Data processing system700may be an example of one system in which root cause analysis system116inFIG. 1may be implemented. In this illustrative example, data processing system700includes communications fabric702, which provides communications between processor unit704, memory706, persistent storage708, communications unit710, input/output unit712, and display714.

Processor unit704serves to execute instructions for software applications and programs that may be loaded into memory706. Processor unit704may be a set of one or more hardware processor devices or may be a multi-processor core, depending on the particular implementation. Further, processor unit704may be implemented using one or more heterogeneous processor systems, in which a main processor is present with secondary processors on a single chip. As another illustrative example, processor unit704may be a symmetric multi-processor system containing multiple processors of the same type.

Input/output unit712allows for the input and output of data with other devices that may be connected to data processing system700. For example, input/output unit712may provide a connection for user input through a keypad, keyboard, and/or some other suitable input device. Display714provides a mechanism to display information to a user and may include touch screen capabilities to allow the user to make on-screen selections through user interfaces or input data, for example.

Instructions for the operating system, applications, and/or programs may be located in storage devices716, which are in communication with processor unit704through communications fabric702. In this illustrative example, the instructions are in a functional form on persistent storage708. These instructions may be loaded into memory706for running by processor unit704. The processes of the different embodiments may be performed by processor unit704using computer-implemented program instructions, which may be located in a memory, such as memory706. These program instructions are referred to as program code, computer-usable program code, or computer-readable program code that may be read and run by a processor in processor unit704. The program code, in the different embodiments, may be embodied on different physical computer-readable storage devices, such as memory706or persistent storage708.

Program code718is located in a functional form on computer-readable media720that is selectively removable and may be loaded onto or transferred to data processing system700for running by processor unit704. Program code718and computer-readable media720form computer program product722. In one example, computer-readable media720may be computer-readable storage media724or computer-readable signal media726. Computer-readable storage media724may include, for example, an optical or magnetic disc that is inserted or placed into a drive or other device that is part of persistent storage708for transfer onto a storage device, such as a hard drive, that is part of persistent storage708. Computer-readable storage media724also may take the form of a persistent storage, such as a hard drive, a thumb drive, or a flash memory that is connected to data processing system700. In some instances, computer-readable storage media724may not be removable from data processing system700.

Alternatively, program code718may be transferred to data processing system700using computer-readable signal media726. Computer-readable signal media726may be, for example, a propagated data signal containing program code718. For example, computer-readable signal media726may be an electro-magnetic signal, an optical signal, and/or any other suitable type of signal. These signals may be transmitted over communication links, such as wireless communication links, an optical fiber cable, a coaxial cable, a wire, and/or any other suitable type of communications link. In other words, the communications link and/or the connection may be physical or wireless in the illustrative examples. The computer-readable media also may take the form of non-tangible media, such as communication links or wireless transmissions containing the program code.

In some illustrative embodiments, program code718may be downloaded over a network to persistent storage708from another device or data processing system through computer-readable signal media726for use within data processing system700. For instance, program code stored in a computer-readable storage media in a data processing system may be downloaded over a network from the data processing system to data processing system700. The data processing system providing program code718may be a server computer, a client computer, or some other device capable of storing and transmitting program code718.

As another example, a computer-readable storage device in data processing system700is any hardware apparatus that may store data. Memory706, persistent storage708, and computer-readable storage media724are examples of physical storage devices in a tangible form.