• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations

Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering (0)

by Z Huang, H Chen, D Zeng
Venue:ACM Trans. Inf. Syst
Add To MetaCart

Tools

Sorted by:
Results 1 - 10 of 131
Next 10 →

Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

by Gediminas Adomavicius, Alexander Tuzhilin - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
Abstract - Cited by 1420 (21 self) - Add to MetaCart
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
(Show Context)

Citation Context

...n. This extension of traditional collaborative filtering techniques is sometimes called “demographic filtering” [76]. Another approach that also explores similarities among users has been proposed in =-=[49]-=-, where the sparsity problem is addressed by applying associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past ...

Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach

by Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, Alexander Tuzhilin - ACM Transactions on Information Systems , 2005
"... The paper presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, exten ..."
Abstract - Cited by 224 (8 self) - Add to MetaCart
The paper presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, extensive profiling, and hierarchical aggregation of recommendations. The paper also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the paper introduces a combined rating estimation method that identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the paper presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance. 1 1.

A Survey of Collaborative Filtering Techniques

by Xiaoyuan Su, Taghi M. Khoshgoftaar , 2009
"... As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenge ..."
Abstract - Cited by 205 (0 self) - Add to MetaCart
As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

Random-walk computation of similarities between nodes of a graph, with application to collaborative recommendation

by François Fouss, Alain Pirotte, Jean-michel Renders, Marco Saerens - IEEE Transactions on Knowledge and Data Engineering , 2006
"... Abstract—This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted and undirected graph. It is based on a Markov-chain model of random walk through the database. More precisely, we compute quantities (the average comm ..."
Abstract - Cited by 188 (19 self) - Add to MetaCart
Abstract—This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted and undirected graph. It is based on a Markov-chain model of random walk through the database. More precisely, we compute quantities (the average commute time, the pseudoinverse of the Laplacian matrix of the graph, etc.) that provide similarities between any pair of nodes, having the nice property of increasing when the number of paths connecting those elements increases and when the “length ” of paths decreases. It turns out that the square root of the average commute time is a Euclidean distance and that the pseudoinverse of the Laplacian matrix is a kernel matrix (its elements are inner products closely related to commute times). A principal component analysis (PCA) of the graph is introduced for computing the subspace projection of the node vectors in a manner that preserves as much variance as possible in terms of the Euclidean commute-time distance. This graph PCA provides a nice interpretation to the “Fiedler vector, ” widely used for graph partitioning. The model is evaluated on a collaborativerecommendation task where suggestions are made about which movies people should watch based upon what they watched in the past. Experimental results on the MovieLens database show that the Laplacian-based similarities perform well in comparison with other methods. The model, which nicely fits into the so-called “statistical relational learning ” framework, could also be used to compute document or word similarities, and, more generally, it could be applied to machine-learning and pattern-recognition tasks involving a relational database. Index Terms—Graph analysis, graph and database mining, collaborative recommendation, graph kernels, spectral clustering, Fiedler vector, proximity measures, statistical relational learning. 1
(Show Context)

Citation Context

...d Laplacian kernel, as introduced by Smola and Kondor [64]. 4. From an experimental point of view, we show that these quantities can be used in the context of collaborative recommendation [18], [32], =-=[35]-=-. Indeed, all the introduced concepts are illustrated on a collaborative-recommendation task where movies are suggested for people to watch from a database of previously watched movies. In particular,...

Unifying user-based and item-based collaborative filtering approaches by similarity fusion

by Jun Wang, Arjen P. De Vries, Marcel J. T. Reinders - In SIGIR ’06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , 2006
"... Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large number of ratings from similar users or similar items are not available, due to the sparsity inherent to rating data. Cons ..."
Abstract - Cited by 110 (11 self) - Add to MetaCart
Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large number of ratings from similar users or similar items are not available, due to the sparsity inherent to rating data. Consequently, prediction quality can be poor. This paper reformulates the memory-based collaborative filtering problem in a generative probabilistic framework, treating individual user-item ratings as predictors of missing ratings. The final rating is estimated by fusing predictions from three sources: predictions based on ratings of the same item by other users, predictions based on different item ratings made by the same user, and, third, ratings predicted based on data from other but similar users rating other but similar items. Existing user-based and item-based approaches correspond to the two simple cases of our framework. The complete model is however more robust to data sparsity, because the different types of ratings are used in concert, while additional ratings from similar users towards similar items are employed as a background model to smooth the predictions. Experiments demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendations.

Scalable collaborative filtering using cluster-based smoothing

by Gui-rong Xue, Chenxi Lin, Qiang Yang, Wensi Xi, Hua-jun Zeng, Yong Yu, Zheng Chen - In Proc. of SIGIR , 2005
"... Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approaches have been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, th ..."
Abstract - Cited by 109 (7 self) - Add to MetaCart
Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approaches have been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approaches have been proposed to alleviate these problems, but these approaches tends to limit the range of users. In this paper, we present a novel approach that combines the advantages of these two kinds of approaches by introducing a smoothing-based method. In our approach, clusters generated from the training data provide the basis for data smoothing and neighborhood selection. As a result, we provide higher accuracy as well as increased efficiency in recommendations. Empirical studies on two datasets (EachMovie and MovieLens) show that our new proposed approach consistently outperforms other state-of-the-art collaborative filtering algorithms.
(Show Context)

Citation Context

...ver, potentially useful information might be lost during this reduction process. By considering the association between users and items, transitive associations of the associative-retrieval technique =-=[11]-=- are proposed to iteratively reinforce the similarity of the users and the similarity of items. Content-boosted CF [1][5] approaches require additional information regarding items as well as a metric ...

Recommender systems with social regularization

by Hao Ma, Michael R. Lyu, Dengyong Zhou, Irwin King, Chao Liu - In WSDM , 2011
"... Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix facto ..."
Abstract - Cited by 89 (5 self) - Add to MetaCart
Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.
(Show Context)

Citation Context

..., (2) trust-aware recommender systems which have drawn lots of attention recently, and (3) social recommender systems which we study in this paper. 2.1 Traditional Recommender Systems As mentioned in =-=[10]-=-, one of the most commonly-used and successfully-deployed recommendation approaches is collaborative filtering. In the field of collaborative filtering, two types of methods are widely studied: neighb...

A Comprehensive Survey of Neighborhood-based Recommendation Methods

by Christian Desrosiers, George Karypis , 2011
"... Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-b ..."
Abstract - Cited by 63 (0 self) - Add to MetaCart
Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and a few solutions to overcome these problems are presented.

Toward Alternative Metrics of Journal Impact: A Comparison of Download and Citation Data

by Johan Bollen, Herbert Van De Sompel, Joan A. Smith, Rick Luce - Information Processing & Management , 2005
"... comparison of download and citation data. ..."
Abstract - Cited by 53 (11 self) - Add to MetaCart
comparison of download and citation data.
(Show Context)

Citation Context

...er download sequences and use these to determine journal relationships. Such an approach is strongly related to item-based collaborative filtering techniques (Sarwar, Karypis, Konstan, & Reidl, 2001; =-=Huang, Chen, & Zeng, 2004-=-), market basket analysis (Brin, Motwani, & Silverstein, 1997), and clickstream data mining (Yan, Jacobsen, Garcia-Molina, & Dayal, 1996; Mathe & Chen, 1996; Xiao & Dunham, 2001) which analyse user do...

Tag recommendations based on tensor dimensionality reduction

by Panagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos - In RecSys ’08: Proc. of the ACM Conference on Recommender systems, 43–50 , 2008
"... Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming ..."
Abstract - Cited by 52 (1 self) - Add to MetaCart
Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming to develop a common consensus about which tags best describe an item. However, they fail to provide appropriate tag recommendations, because: (i) users may have different interests for an information item and (ii) information items may have multiple facets. In contrast to the current tag recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.
(Show Context)

Citation Context

... training set and a test set with sizes 75% and 25% of the original set, respectively. All algorithms had the task to predict the tags of the users’ postings in the test set. Based on the approach of =-=[9, 7]-=-, a more realistic evaluation of recommendation should consider the division of tags of each test user into two sets: (i) the past tags of the test user and, (ii) the future tags of the test user. The...

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2018 The Pennsylvania State University