Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the “new user problem”. Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.

Systems for automatically recommending items (e.g., movies, products, or information) to users are becoming increasingly important in e-commerce applications, digital libraries, and other domains where mass personalization is highly valued. Such recommender systems typically base their suggestions on (1) collaborative data encoding which users like which items, and/or (2) content data describing item features and user demographics. Systems that rely solely on collaborative data fail when operating from a cold start|that is, when recommending items (e.g., rst-run movies) that no member of the community has yet seen. We develop several generative probabilistic models that circumvent the cold-start problem by mixing content data with collaborative data in a sound statistical manner. We evaluate the algorithms using MovieLens movie ratings data, augmented with actor and director information from the Internet Movie Database. We nd that maximum likelihood learning with the expectation maximization (EM) algorithm and variants tends to over t complex models that are initialized randomly. However, by seeding parameters of the complex models with parameters learned in simpler models, we obtain greatly improved performance. We explore both methods that exploit a single type of content data (e.g., actors only) and methods that leverage multiple types of content data (e.g., both actors and directors) simultaneously.

Since many of today's application software provide users with too many functions, the users sometimes cannot find the useful functions. This paper proposes a recommendation system based on a collaborative filtering approach to let users discover useful functions at low cost for the purpose of improving the user's productivity in using application software. The proposed system automatically collects histories of software function execution (usage histories)from many users through the Internet. Based on the collaborative filtering approach, collected histories are used to recommend the user a set of candidate functions that may be useful to the individual user. This paper illustrates conventional filtering algorithms and proposes a new algorithm suitable for recommendation of software functions. The result of an experiment with a prototype recommendation system showed that the average ndpm of our algorithm was smaller than that of the conventional algorithms; and, it also showed that the standard deviation of ndpm of our algorithm was smaller than that of the conventional algorithms. Furthermore, while every conventional algorithm had a case whose recommendation was worse than the random algorithm, our algorithm did not.

Recommender systems have become paramount to customize information access and reduce information overload. They serve multiple uses, ranging from suggesting products and artifacts (to consumers), to bringing people together by the connections induced by (similar) reactions to products and services. This thesis presents a graph-theoretic model that casts recommendation as a process of ‘jumping connections’ in a graph. In addition to emphasizing the social network aspect, this viewpoint provides a novel evaluation criterion for recommender systems. Algorithms for recommender systems are distinguished not in terms of predicted ratings of services/artifacts, but in terms of the combinations of people and artifacts that they bring together. We present an algorithmic framework drawn from random graph theory and outline an analysis for one particular form of jump called a ‘hammock.’ Experimental results on two datasets collected over the Internet demonstrate the validity of this approach.

Collaborative Filtering (CF)-based recommender systems are indispensable tools to find items of interest from the unmanageable number of available items. Moreover, companies who deploy a CF-based recommender system may be able to increase revenue by drawing customers ’ attention to items that they are likely to buy. However, the sheer number of customers and items typical in e-commerce systems demand specially designed CF algorithms that can gracefully cope with the vast size of the data. Many algorithms proposed thus far, where the principal concern is recommendation quality, may be too expensive to operate in a large-scale system. We propose ClustKnn, a simple and intuitive algorithm that is well suited for large data sets. The method first compresses data tremendously by building a straightforward but efficient clustering model. Recommendations are then generated quickly by using a simple Nearest Neighbor-based approach. We demonstrate the feasibility of ClustKnn both analytically and empirically. We also show, by comparing with a number of other popular CF algorithms that, apart from being highly scalable and intuitive, ClustKnn provides very good recommendation accuracy as well.

A recommender system must be able to suggest items that are likely to be preferred by the user. In most systems, the degree of preference is represented by a rating score. Given a database of users ’ past ratings on a set of items, traditional collaborative filtering algorithms are based on predicting the potential ratings that a user would assign to the unrated items so that they can be ranked by the predicted ratings to produce a list of recommended items. In this paper, we propose a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings. We measure the similarity between users based on the correlation between their rankings of the items rather than the rating values and propose new collaborative filtering algorithms for ranking items based on the preferences of similar users. Experimental results on real world movie rating data sets show that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.

Intelligent agents often need to assess user utility functions in order to make decisions on their behalf, or predict their behavior. When uncertainty exists over the precise nature of this utility function, one can model this uncertainty using a distribution over utility functions. This view lies at the core of games with incomplete information and, more recently, several proposals for incremental preference elicitation. In such cases, decisions (or predicted behavior) are based on computing the expected expected utility (EEU) of decisions with respect to the distribution over utility functions. Unfortunately, decisions made under EEU are sensitive to the precise representation of the utility function. We examine the conditions under which EEU provides for sensible decisions by appeal to the foundational axioms of decision theory. We also discuss the impact these conditions have on the enterprise of preference elicitation more broadly.

This paper introduces Scienstein, the first hybrid research paper recommender system and a powerful alternative to currently used academic search engines. Scienstein improves the approach of the usually used keyword-based search by combining it with citation analysis, author analysis, source analysis, implicit ratings, explicit ratings and in addition, innovative and yet unused methods like the ‘Distance Similarity Index ’ (DSI) and the ‘In-text Impact Factor ’ (ItIF).

parkst @ yahoo-inc.com We propose a new ranking method, which combines recommender systems with information search tools for better search and browsing. Our method uses a collaborative filtering algorithm to generate personal item authorities for each user and combines them with item proximities for better ranking. To demonstrate our approach, we build a prototype movie search and browsing engine called MAD6 (Movies, Actors and Directors; 6 degrees of separation). We conduct offline and online tests of our ranking algorithm. For offline testing, we use Yahoo! Search queries that resulted in a click on a Yahoo! Movies or Internet Movie Database (IMDB) movie URL. Our online test involved 44 Yahoo! employees providing subjective assessments of results quality. In both tests, our ranking methods show significantly better recall and quality than IMDB search and Yahoo! Movies current search.

Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, although these approaches all suffer from data sparsity and poor prediction quality problems. Usually, the user-item matrix is quite sparse, which directly leads to inaccurate recommendations. This paper focuses the memory-based collaborative filtering problems on two crucial factors: (1) similarity computation between users or items and (2) missing data prediction algorithms. First, we use the enhanced Pearson Correlation Coefficient (PCC) algorithm by adding one parameter which overcomes the potential decrease of accuracy when computing the similarity of users or items. Second, we propose an effective missing data prediction algorithm, in which information of both users and items is taken into account. In this algorithm, we set the similarity threshold for users and items respectively, and the prediction algorithm will determine whether predicting the missing data or not. We also address how to predict the missing data by employing a combination of user and item information. Finally, empirical studies on dataset MovieLens have shown that our newly proposed method outperforms other stateof-the-art collaborative filtering algorithms and it is more robust against data sparsity.