Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation and SVD based methods provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings where the known preference information does not change with time. However, a number of practical scenarios require dynamic real-time collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel collaborative filtering approach based on a recently proposed weighted co-clustering algorithm [3] that involves simultaneous clustering of users and items. We design incremental and parallel versions of the co-clustering algorithm and use it to build an efficient real-time collaborative filtering framework. Empirical evaluation of our approach on large movie and book rating datasets demonstrates that it is possible to obtain an accuracy comparable to that of the correlation and matrix factorization based approaches at a much lower computational cost. 1

We present and evaluate methods for diversifying search results to improve personalized web search. A common personalization approach involves reranking the top N search results such that documents likely to be preferred by the user are presented higher. The usefulness of reranking is limited in part by the number and diversity of results considered. We propose three methods to increase the diversity of the top results and evaluate the effectiveness of these methods.

Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diversify results to improve user satisfaction. In such a setting, the probability ranking principle-based approach of presenting the most relevant results on top can be sub-optimal, and hence the search engine would like to trade-off relevance for diversity in the results. In analogy to prior work on ranking and clustering systems, we use the axiomatic approach to characterize and design diversification systems. We develop a set of natural axioms that a diversification system is expected to satisfy, and show that no diversification function can satisfy all the axioms simultaneously. We illustrate the use of the axiomatic framework by providing three example diversification objectives that satisfy different subsets of the axioms. We also uncover a rich link to the facility dispersion problem that results in algorithms for a number of diversification objectives. Finally, we propose an evaluation methodology to characterize the objectives and the underlying axioms. We conduct a large scale evaluation of our objectives based on two data sets: a data set derived from the Wikipedia disambiguation pages and a product database.

Based on our recent work on the development of a trust model for recommender agents and a qualitative survey, we explore the potential of building users ’ trust with explanation interfaces. We present the major results from the survey, which provided a roadmap identifying the most promising areas for investigating design issues for trust-inducing interfaces. We then describe a set of general principles derived from an in-depth examination of various design dimensions for constructing explanation interfaces, which most contribute to trust formation. We present results of a significantscale user study, which indicate that the organization-based explanation is highly effective in building users ’ trust in the recommendation interface, with the benefit of increasing users’ intention to return to the agent and save cognitive effort. Categories and Subject Descriptors H.1.2 [Models and Principles]: User/Machine Systems – human

The internet presents people with an increasingly bewildering variety of choices. Online consumers have to rely on computerized search tools to find the most preferred option in a reasonable amount of time. Recommender systems address this problem by searching for options based on a model of the user’s preferences. We consider example critiquing as a methodology for mixedinitiative recommender systems. In this technique, users volunteer their preferences as critiques on examples. It is thus important to stimulate their preference expression by selecting the proper examples, called suggestions. We describe the look-ahead principle for suggestions and describe several suggestion strategies based on it. We compare them in simulations and, for the first time, report a set of user studies which prove their effectiveness in increasing users ’ decision accuracy by up to 75%. Author Keywords Recommender systems, consumer decision support, example

We study the problem of efficiently computing diverse query results in online shopping applications, where users specify queries through a form interface that allows a mix of structured and content-based selection conditions. Intuitively, the goal of diverse query answering is to return a representative set of top-k answers from all the tuples that satisfy the user selection condition. For example, if a user is searching for Honda cars and we can only display five results, we wish to return cars from five different Honda models, as opposed to returning cars from only one or two Honda models. A key contribution of this paper is to formally define the notion of diversity, and to show that existing score based techniques commonly used in web applications are not sufficient to guarantee diversity. Another contribution of this paper is to develop novel and efficient query processing techniques that guarantee diversity. Our experimental results using Yahoo! Autos data show that our proposed techniques are scalable and efficient. I.

Abstract. In this chapter we present an overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle. These phases include data collection and preprocessing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This view of the personalization process provides added flexibility in leveraging multiple data sources and in effectively using the discovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activities and techniques used at different stages of this cycle, including the preprocessing and integration of data from multiple sources, as well as pattern discovery techniques that are typically applied to this data. We consider a number of classes of data mining algorithms used particularly for Web personalization, including techniques based on clustering, association rule discovery, sequential pattern mining, Markov models, and probabilistic mixture and hidden (latent) variable models. Finally, we discuss hybrid data mining frameworks that leverage data from a variety

Abstract. In this chapter we provide a comprehensive overview of the topic of Intelligent Techniques for Web Personalization. Web Personalization is viewed as an application of data mining and machine learning techniques to build models of user behaviour that can be applied to the task of predicting user needs and adapting future interactions with the ultimate goal of improved user satisfaction. This chapter survey’s the state-of-the-art in Web personalization. We start by providing a description of the personalization process and a classification of the current approaches to Web personalization. We discuss the various sources of data available to personalization systems, the modelling approaches employed and the current approaches to evaluating these systems. A number of challenges faced by researchers developing these systems are described as are solutions to these challenges proposed in literature. The chapter concludes with a discussion on the open challenges that must be addressed by the research community if this technology is to make a positive impact on user satisfaction with the Web. 1

Recommendations in collaborative tagging sites such as del.icio.us and Yahoo! Movies, are becoming increasingly important, due to the proliferation of general queries on those sites and the ineffectiveness of the traditional search paradigm to address those queries. Regardless of the underlying recommendation strategy, item-based or user-based, one of the key concerns in producing recommendations, is over-specialization, which results in returning items that are too homogeneous. Traditional solutions rely on post-processing returned items to identify those which differ in their attribute values (e.g., genre and actors for movies). Such approaches are not always applicable when intrinsic attributes are not available (e.g., URLs in del.icio.us). In a recent paper [20], we introduced the notion of explanation-based diversity and formalized the diversification problem as a compromise between accuracy and diversity. In this paper, we develop efficient diversification algorithms built upon this notion. The algorithms explore compromises between accuracy and diversity. We demonstrate their efficiency and effectiveness in diversification on two real life data sets: del.icio.us and Yahoo! Movies. 1.

Collaboration on Web search is common in many domains, such as education and knowledge work; recently, HCI researchers have begun to introduce prototype collaborative search tools to support such scenarios. We analyze data from a collaborative search experiment, and based on these data we propose three techniques that can enhance the value of collaborative search tools using personalization: groupization, smart splitting, and group hit-highlighting. Author Keywords Collaborative search, web search, group search. ACM Classification Keywords H5.3. Information interfaces and presentation (e.g., HCI): Group and organization interfaces – computer-supported cooperative work.