OpenRaiser/Intern-Atlas huggingface dataset:
Image annotation based on recommendation model
Authors: Zijia Lin, Guiguang Ding, Jianmin Wang
Venue: SIGIR
Year: 2011

In this paper, a novel approach based on recommendation model is proposed for automatic image annotation. For any to-be-annotated image, we first select some related images with tags from training dataset according to their visual similarity. And then we estimate the initial ratings for tags of the training images based on tag ranking method and construct a rating matrix. We also construct a trust matrix based on visual similarity with a k-NN strategy. Then a recommendation model is built on both matrices to rank candidate tags for the target image. The proposed approach is evaluated using two benchmark image datasets, and experimental results have indicated its effectiveness.
Social annotation in query expansion: a machine learning approach
Authors: Yuan Lin, Hongfei Lin, Song Jin, Zheng Ye
Venue: SIGIR
Year: 2011

Automatic query expansion technologies have been proven to be effective in many information retrieval tasks. Most existing approaches are based on the assumption that the most informative terms in top-retrieved documents can be viewed as context of the query and thus can be used for query expansion. One problem with these approaches is that some of the expansion terms extracted from feedback documents are irrelevant to the query, and thus may hurt the retrieval performance. In social annotations, users provide different keywords describing the respective Web pages from various aspects. These features may be used to boost IR performance. However, to date, the potential of social annotation for this task has been largely unexplored. In this paper, we explore the possibility and potential of social annotation as a new resource for extracting useful expansion terms. In particular, we propose a term ranking approach based on social annotation resource. The proposed approach consists of two phases: (1) in the first phase, we propose a term-dependency method to choose the most likely expansion terms; (2) in the second phase, we develop a machine learning method for term ranking, which is learnt from the statistics of the candidate expansion terms, using ListNet. Experimental results on three TREC test collections show that the retrieval performance can be improved when the term ranking method is used. In addition, we also demonstrate that terms selected by the term-dependency method from social annotation resources are beneficial to improve the retrieval performance.
Best document selection based on approximate utility optimization
Authors: Hungyu Henry Lin, Yi Zhang, James E. Davis
Venue: SIGIR
Year: 2011

This poster describes an alternative approach to handling the best document selection problem. Best document selection is a common problem with many real world applications, but is not a well studied by itself; a simple solution would be to treat it as a ranking problem and to use existing ranking algorithms to rank all documents. We could then select only the first element of the sorted list. However, because ranking models optimize for all ranks, the model may sacrifice accuracy of the top rank for the sake of overall accuracy. This is an unnecessary trade-off. We begin by first defining an appropriate objective function for the domain, then create a boosting algorithm that explicitly targets this function. Based on experiments on a benchmark retrieval data set and Digg.com news commenting data set, we find that even a simple algorithm built for this specific problem gives better results than baseline algorithms that were designed for the more complicated ranking tasks. 1.
Sense discrimination for physics retrieval
Authors: Christina Lioma, Alok Kothari, Hinrich Schütze
Venue: SIGIR
Year: 2011

Information Retrieval in technical domains like physics is characterised by long and precise queries, whose meaning is strongly influenced by term context and domain. We treat this as a disambiguation problem, and present initial findings of a retrieval model that posits a higher probability of relevance for documents matching disambiguated query terms. Preliminary evaluation on a real-life physics test collection shows promising performance improvement.
Search task difficulty: the expected vs. the reflected
Authors: Jingjing Liu, Nicholas J. Belkin
Venue: SIGIR
Year: 2011

We report findings on how the user's perception of task difficulty changes before and after searching for information to solve tasks. We found that while in one type of task, the dependent task, this did not change, in another, the parallel task, it did. The findings have implications on designing systems that can provide assistance to users with their search and task solving strategies.
QuickView: advanced search of tweets
Authors: Xiaohua Liu, Long Jiang, Furu Wei, Ming Zhou
Venue: SIGIR
Year: 2011

Tweets have become a comprehensive repository for real-time information. However, it is often hard for users to quickly get information they are interested in from tweets, owing to the sheer volume of tweets as well as their noisy and informal nature. We present QuickView, an NLP-based tweet search platform to tackle this issue. Specifically, it exploits a series of natural language processing technologies, such as tweet normalization, named entity recognition, semantic role labeling, sentiment analysis, tweet classification, to extract useful information, i.e., named entities, events, opinions, etc., from a large volume of tweets. Then, non-noisy tweets, together with the mined information, are indexed, on top of which two brand new scenarios are enabled, i.e., categorized browsing and advanced search, allowing users to effectively access either the tweets or fine-grained information they are interested in.
Future of the web and search
Authors: Qi Lu
Venue: SIGIR
Year: 2011

No one doubts that we have only scratched the surface of what is possible with the Web. The day is coming fast when the Web will become almost a virtual mind reader. Your intent, interests, and needs will be instantly perceived and the information you want will be promptly delivered -- whether you ask for it directly or not -- based on a deep understanding of the meaning of words in your query, knowledge of your preferences and patterns, what others have done before you, your location, and more. In this talk, I will share some of my thoughts about where the Web is heading and how search will be transformed to align to this new Web, laying out some specifics behind Microsoft's vision to empower people with knowledge.
A boosting approach to improving pseudo-relevance feedback
Authors: Yuanhua Lv, ChengXiang Zhai, Wan Chen
Venue: SIGIR
Year: 2011

Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback helps many queries, it also often hurts many other queries, limiting its usefulness in real retrieval applications. Thus an important, yet difficult challenge is to improve the overall effectiveness of pseudo-relevance feedback without sacrificing the performance of individual queries too much. In this paper, we propose a novel learning algorithm, FeedbackBoost, based on the boosting framework to improve pseudo-relevance feedback through optimizing the combination of a set of basis feedback algorithms using a loss function defined to directly measure both robustness and effectiveness. FeedbackBoost can potentially accommodate many basis feedback methods as features in the model, making the proposed method a general optimization framework for pseudo-relevance feedback. As an application, we apply FeedbackBoost to improve pseudo feedback based on language models through combining different document weighting strategies. The experiment results demonstrate that FeedbackBoost can achieve better average precision and meanwhile dramatically reduce the number and magnitude of feedback failures as compared to three representative pseudo feedback methods and a standard learning to rank approach for pseudo feedback.
Modeling document scores for distributed information retrieval
Authors: Ilya Markov
Venue: SIGIR
Year: 2011

Distributed Information Retrieval (DIR), also known as Federated Search, integrates multiple searchable collections and provides direct access to them through a unified interface [3]. This is done by a centralized broker, that receives user queries, forwards them to appropriate collections and returns merged results to users.
Towards an indexing method to speed-up music retrieval
Authors: Benjamin Martin, Pierre Hanna, Matthias Robine, Pascal Ferraro
Venue: SIGIR
Year: 2011

Computations in most music retrieval systems strongly depend on the size of data compared. We propose to enhance performances of a music retrieval system, namely a harmonic similarity evaluation method, by first indexing relevant parts of music pieces. The indexing algorithm represents each audio piece exclusively by its major repetition, using harmonic descriptions and string matching techniques. Evaluations are performed in the context of a state-of-the-art retrieval method, namely cover songs identification, and results highlight the success of our indexing system in keeping similar results while yielding a substantial gain in computation time.
Descriptive modelling of text classification and its integration with other IR tasks
Authors: Miguel Martinez-Alvarez
Venue: SIGIR
Year: 2011

Nowadays, Information Retrieval (IR) systems have to deal with multiple sources of data available in different formats. Datasets can consist of complex and heterogeneous objects with relationships between them. In addition, information needs can vary wildly and they can include different tasks. As a result, the importance of flexibility in IR systems is rapidly growing. This fact is specially important in environments where the information required at different moments is very different and its utility may be contingent on timely implementation. In these cases, how quickly a new problem is solved is as important as how well you solve it.
Incremental diversification for very large sets: a streaming-based approach
Authors: Enrico Minack, Wolf Siberski, Wolfgang Nejdl
Venue: SIGIR
Year: 2011

Result diversification is an effective method to reduce the risk that none of the returned results satisfies a user's query intention. It has been shown to decrease query abandonment substantially. On the other hand, computing an optimally diverse set is NP-hard for the usual objectives. Existing greedy diversification algorithms require random access to the input set, rendering them impractical in the context of large result sets or continuous data.
Measuring improvement in user search performance resulting from optimal search tips
Authors: Neema Moraveji, Daniel M. Russell, Jacob Bien, David Mease
Venue: SIGIR
Year: 2011

Web search performance can be improved by either improving the search engine itself or by educating the user to search more efficiently. There is a large amount of literature describing techniques for measuring the former; whereas, improvements resulting from the latter are more difficult to quantify. In this paper we demonstrate an experimental methodology that proves to successfully quantify improvements from user education. The user education in our study is realized in the form of tactical search feature tips that expand user awareness of task-relevant tools and features of the search application. Initially, these tips are presented in an idealized situation: each tip is shown at the same time as the study participants are given a task that is constructed to benefit from the specific tip. However, we also present a follow-up study roughly one week later in which the search tips are no longer presented but the study participants who previously were shown search tips still demonstrate improved search efficiency compared to the control group. This research has implications for search user interface designers and the study of information retrieval systems.
Handling data sparsity in collaborative filtering using emotion and semantic based features
Authors: Yashar Moshfeghi, Benjamin Piwowarski, Joemon M. Jose
Venue: SIGIR
Year: 2011

Collaborative filtering (CF) aims to recommend items based on prior user interaction. Despite their success, CF techniques do not handle data sparsity well, especially in the case of the cold start problem where there is no past rating for an item. In this paper, we provide a framework, which is able to tackle such issues by considering item-related emotions and semantic data. In order to predict the rating of an item for a given user, this framework relies on an extension of Latent Dirichlet Allocation, and on gradient boosted trees for the final prediction. We apply this framework to movie recommendation and consider two emotion spaces extracted from the movie plot summary and the reviews, and three semantic spaces: actor, director, and genre. Experiments with the 100K and 1M MovieLens datasets show that including emotion and semantic information significantly improves the accuracy of prediction and improves upon the state-of-the-art CF techniques. We also analyse the importance of each feature space and describe some uncovered latent groups.
Enriching document representation via translation for improved monolingual information retrieval
Authors: Seung-Hoon Na, Hwee Tou Ng
Venue: SIGIR
Year: 2011

Word ambiguity and vocabulary mismatch are critical problems in information retrieval. To deal with these problems, this paper proposes the use of translated words to enrich document representation, going beyond the words in the original source language to represent a document. In our approach, each original document is automatically translated into an auxiliary language, and the resulting translated document serves as a semantically enhanced representation for supplementing the original bag of words. The core of our translation representation is the expected term frequency of a word in a translated document, which is calculated by averaging the term frequencies over all possible translations, rather than focusing on the 1-best translation only. To achieve better efficiency of translation, we do not rely on full-fledged machine translation, but instead use monotonic translation by removing the time-consuming reordering component. Experiments carried out on standard TREC test collections show that our proposed translation representation leads to statistically significant improvements over using only the original language of the document collection.
Rating prediction using feature words extracted from customer reviews
Authors: Masanao Ochi, Makoto Okabe, Rikio Onai
Venue: SIGIR
Year: 2011

We developed a simple method of improving the accuracy of rating prediction using feature words extracted from customer reviews. Many rating predictors work well for a small and dense dataset of customer reviews. However, a practical dataset tends to be large and sparse, because it often includes too many products for each customer to buy and evaluate. Data sparseness reduces prediction accuracy. To improve accuracy, we reduced the dimension of the feature vector using feature words extracted by analyzing the relationship between ratings and accompanying review comments instead of using ratings. We applied our method to the Pranking algorithm and evaluated it on a corpus of golf course reviews supplied by a Japanese e-commerce company. We found that by successfully reducing data sparseness, our method improves prediction accuracy as measured using RankLoss.
A novel corpus-based stemming algorithm using co-occurrence statistics
Authors: Jiaul H. Paik, Dipasree Pal, Swapan K. Parui
Venue: SIGIR
Year: 2011

We present a stemming algorithm for text retrieval. The algorithm uses the statistics collected on the basis of certain corpus analysis based on the co-occurrence between two word variants. We use a very simple co-occurrence measure that reflects how often a pair of word variants occurs in a document as well as in the whole corpus. A graph is formed where the word variants are the nodes and two word variants form an edge if they co-occur. On the basis of the co-occurrence measure, a certain edge strength is defined for each of the edges. Finally, on the basis of the edge strengths, we propose a partition algorithm that groups the word variants based on their strongest neighbors, that is, the neighbors with largest strengths.
Inverted indexes for phrases and strings
Authors: Manish Patil, Sharma V. Thankachan, Rahul Shah, Wing-Kai Hon, Jeffrey Scott Vitter, Sabrina Chandrasekaran
Venue: SIGIR
Year: 2011

Inverted indexes are the most fundamental and widely used data structures in information retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often applied to further reduce the space requirement of these lists. However, the index has a shortcoming, in that only predefined pattern queries can be supported efficiently. In terms of string documents where word boundaries are undefined, if we have to index all the substrings of a given document, then the storage quickly becomes quadratic in the data size. Also, if we want to apply the same type of indexes for querying phrases or sequence of words, then the inverted index will end up storing redundant information. In this paper, we show the first set of inverted indexes which work naturally for strings as well as phrase searching. The central idea is to exclude document d in the inverted list of a string P if every occurrence of P in d is subsumed by another string of which P is a prefix. With this we show that our space utilization is close to the optimal. Techniques from succinct data structures are deployed to achieve compression while allowing fast access in terms of frequency and document id based retrieval. Compression and speed trade-offs are evaluated for different variants of the proposed index. For phrase searching, we show that our indexes compare favorably against a typical inverted index deploying position-wise intersections. We also show efficient top-k based retrieval under relevance metrics like frequency and tf-idf.
JuSe: a picture dictionary query system for children
Authors: Tamara Polajnar, Richard Glassey, Leif Azzopardi
Venue: SIGIR
Year: 2011

As adults we take for granted our capacity to express our information needs verbally and textually. However, young children also have preferences and information needs, but are just learning to be able to express themselves effectively. Consequently they encounter many barriers when trying to spell, type, and communicate their needs to a 'faceless' search engine text box.
Learning to rank under tight budget constraints
Authors: Christian Pölitz, Ralf Schenkel
Venue: SIGIR
Year: 2011

This paper investigates the influence of pruning feature lists to keep a given budget for the evaluation of ranking methods. We learn from a given training set how important the individual prefixes are for the ranking quality. Based on there importance we choose the best prefixes to calculate the ranking while keeping the budget.
Graph-cut based tag enrichment
Authors: Xueming Qian, Xian-Sheng Hua
Venue: SIGIR
Year: 2011

In this paper, a graph cut based tag enrichment approach is proposed. We build a graph for each image with its initial tags. The graph is with two terminals. Nodes of the graph are full connected with each other. Min-cut/max-flow algorithm is utilized to find the relevant tags for the image. Experiments on Flickr dataset demonstrate the effectiveness of the proposed graph-cut based tag enrichment approach.
Practical online retrieval evaluation
Authors: Filip Radlinski, Yisong Yue
Venue: SIGIR
Year: 2011

Online evaluation is amongst the few evaluation techniques available to the information retrieval community that is guaranteed to reflect how users actually respond to improvements developed by the community. Broadly speaking, online evaluation refers to any evaluation of retrieval quality conducted while observing user behavior in a natural context. However, it is rarely employed outside of large commercial search engines due primarily to a perception that it is impractical at small scales. The goal of this tutorial is to familiarize information retrieval researchers with state-of-the-art techniques in evaluating information retrieval systems based on natural user clicking behavior, as well as to show how such methods can be practically deployed. In particular, our focus will be on demonstrating how the Interleaving approach and other click based techniques contrast with traditional offline evaluation, and how these online methods can be effectively used in academic-scale research. In addition to lecture notes, we will also provide sample software and code walk-throughs to showcase the ease with which Interleaving and other click-based methods can be employed by students, academics and other researchers.
Personalized video: leanback online video consumption
Authors: Krishnan Ramanathan, Yogesh Sankarasubramaniam, Vidhya Govindaraju
Venue: SIGIR
Year: 2011

Current user interfaces for online video consumption are mostly browser based, lean forward, require constant interaction and provide a fragmented view of the total content available. For easier consumption, the user interface and interactions need to be redesigned for less interruptive and lean back experience. In this paper, we describe Personalized Video, an application that converts the online video experience into a personalized lean back experience. It has been implemented on the Windows platform and integrated with intuitive user interactions like gesture and face recognition. It also supports group personalization for concurrent users.
GreenMeter: a tool for assessing the quality and recommending tags for web 2.0 applications
Authors: Saulo M. R. Ricci, Dilson Almeida Guimarães, Fabiano Muniz Belém, Jussara M. Almeida, Marcos André Gonçalves, Raquel Oliveira Prates
Venue: SIGIR
Year: 2011

We present GreenMeter, a tool for assessing the quality and recommending tags for Web 2.0 content. Its goal is to improve tag quality and the effectiveness of various information services (e.g., search, content recommendation) that rely on tags as data sources. We demonstrate an implementation of GreenMeter for the popular Last.fm application.
Enhancing multi-label music genre classification through ensemble techniques
Authors: Chris Sanden, John Z. Zhang
Venue: SIGIR
Year: 2011

In the field of Music Information Retrieval (MIR), multi-label genre classification is the problem of assigning one or more genre labels to a music piece. In this work, we propose a set of ensemble techniques, which are specific to the task of multi-label genre classification. Our goal is to enhance classification performance by combining multiple classifiers. In addition, we also investigate some existing ensemble techniques from machine learning. The effectiveness of these techniques is demonstrated through a set of empirical experiments and various related issues are discussed. To the best of our knowledge, there has been limited work on applying ensemble techniques to multi-label genre classification in the literature and we consider the results in this work as our initial efforts toward this end. The significance of our work has two folds: (1) proposing a set of ensemble techniques specific to music genre classification and (2) shedding light on further research along this direction.