Publications: Learning for Recommender Systems

1. Review Quality Aware Collaborative Filtering
   [Details] [PDF]
   Sindhu Raghavan and Suriya Ganasekar and Joydeep Ghosh
   In Sixth ACM Conference on Recommender Systems (RecSys 2012), 123--130, September 2012.

   Probabilistic matrix factorization (PMF) and other popular approaches to collaborative filtering assume that the ratings given by users for products are genuine, and hence they give equal importance to all available ratings. However, this is not always true due to several reasons including the presence of opinion spam in product reviews. In this paper, the possibility of performing collaborative filtering while attaching weights or quality scores to the ratings is explored. The quality scores, which are determined from the corresponding review data are used to ``up--weight'' or ``down--weight'' the importance given to the individual rating while performing collaborative filtering, thereby improving the accuracy of the predictions. First, the measure used to capture the quality of the ratings is described. Different approaches for estimating the quality score based on the available review information are examined. Subsequently, a mathematical formulation to incorporate quality scores as weights for the ratings in the basic PMF framework is derived. Experimental evaluation on two product categories of a benchmark data set from Amazon.com demonstrates the efficacy of our approach.

   ML ID: 281

2. Explaining Recommendations: Satisfaction vs. Promotion
   [Details] [PDF]
   Mustafa Bilgic and Raymond J. Mooney
   In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, January 2005.

   Recommender systems have become a popular technique for helping users select desirable books, movies, music and other items. Most research in the area has focused on developing and evaluating algorithms for efficiently producing accurate recommendations. However, the ability to effectively explain its recommendations to users is another important aspect of a recommender system. The only previous investigation of methods for explaining recommendations showed that certain styles of explanations were effective at convincing users to adopt recommendations (i.e. promotion) but failed to show that explanations actually helped users make more accurate decisions (i.e. satisfaction). We present two new methods for explaining recommendations of content-based and/or collaborative systems and experimentally show that they actually improve user's estimation of item quality.

   ML ID: 156

3. Explanation for Recommender Systems: Satisfaction vs. Promotion
   [Details] [PDF]
   Mustafa Bilgic
   Austin, TX, May 2004. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.

   There is much work done on Recommender Systems, systems that automate the recommendation process; however there is little work done on explaining recommendations. The only study we know did an experiment measuring which explanation system increased user's acceptance of the item how much (promotion). We took a different approach and measured which explanation system estimated the true quality of the item the best so that the user can be satisfied with the selection in the end (satisfaction).

   ML ID: 142

4. Content-Boosted Collaborative Filtering for Improved Recommendations
   [Details] [PDF]
   Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
   In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), 187-192, Edmonton, Alberta, 2002.

   Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.

   ML ID: 114

5. Content-Boosted Collaborative Filtering
   [Details] [PDF]
   Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
   In Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001.

   Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendattions in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach. We also discuss methods to improve the performance of our hybrid system.

   ML ID: 108

6. Content-Based Book Recommending Using Learning for Text Categorization
   [Details] [PDF]
   Raymond J. Mooney and Loriene Roy
   In Proceedings of the Fifth ACM Conference on Digital Libraries, 195-204, San Antonio, TX, June 2000.

   Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

   ML ID: 98

7. Content-Based Book Recommending Using Learning for Text Categorization
   [Details] [PDF]
   Raymond J. Mooney and Loriene Roy
   In Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.

   Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use social filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommended previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations. These experiments are based on ratings from random samplings of items and we discuss problems with previous experiments that employ skewed samples of user-selected examples to evaluate performance.

   ML ID: 96

8. Book Recommending Using Text Categorization with Extracted Information
   [Details] [PDF]
   Raymond J. Mooney, Paul N. Bennett, and Loriene Roy
   In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)"-REC-WKSHP98, year="1998, 70-74, Madison, WI, 1998.

   Content-based recommender systems suggest documents, items, and services to users based on learning a profile of the user from rated examples containing information about the given items. Text categorization methods are very useful for this task but generally rely on unstructured text. We have developed a book-recommending system that utilizes semi-structured information about items gathered from the web using simple information extraction techniques. Initial experimental results demonstrate that this approach can produce fairly accurate recommendations.

   ML ID: 86

9. Text Categorization Through Probabilistic Learning: Applications to Recommender Systems
   [Details] [PDF]
   Paul N. Bennett
   1998. Honors thesis, Department of Computer Sciences, The University of Texas at Austin.

   With the growth of the World Wide Web, recommender systems have received an increasing amount of attention. Many recommender systems in use today are based on collaborative filtering. This project has focused on LIBRA, a content-based book recommending system. By utilizing text categorization methods and the information available for each book, the system determines a user profile which is used as the basis of recommendations made to the user. Instead of the bag-of-words approach used in many other statistical text categorization approaches, LIBRA parses each text sample into a semi-structured representation. We have used standard Machine Learning techniques to analyze the performance of several algorithms on this learning task. In addition, we analyze the utility of several methods of feature construction and selection (i.e. methods of choosing the representation of an item that the learning algorithm actually uses). After analyzing the system we conclude that good recommendations are produced after a relatively small number of training examples. We also conclude that the feature selection method tested does not improve the performance of these algorithms in any systematic way, though the results indicate other feature selection methods may prove useful. Feature construction, however, while not providing a large increase in performance with the particular construction methods used here, holds promise of providing performance improvements for the algorithms investigated. This text assumes only minor familiarity with concepts of artificial intelligence and should be readable by the upper division computer science undergraduate familiar with basic concepts of probability theory and set theory.

   ML ID: 85