In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches.

Consider a conversational product recommender system in which a user repeatedly edits and resubmits a query until she finds a product that she wants. We show how an advisor can: observe the user’s actions; infer constraints on the user’s utility function and add them to a user model; use the constraints to deduce which queries the user is likely to try next; and advise the user to avoid those that are unsatisfiable. We call this information recommendation. We give a detailed formulation of information recommendation for the case of products that are described by a set of Boolean features. Our experimental results show that if the user is given advice, the number of queries she needs to try before finding the product of highest utility is greatly reduced. We also show that an advisor that confines its advice to queries that the user model predicts are likely to be tried next will give shorter advice than one whose advice is unconstrained by the user model.

Electronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved in the integration with modeling, and various research areas that may supply parts of an ultimate solution. We also present and demonstrate briefly a unified framework within which one can integrate acquisitions of different types, with any cost structure and any predictive modeling objective.

In this paper we report on a pilot user study aimed at evaluating two aspects of recommender systems that have not been the aim of previous user studies in the field. Firstly, item semantics may be incorporated into a collaborative recommender system and we wish to measure the effect on user satisfaction. Secondly, we would like to evaluate different approaches to collecting ratings from users: the ratings that are used to seed their profile with a collaborative filtering system. Key indications from the study are: users do prefer recommendations generated by semantically enhanced recommender systems; the user’s satisfaction with a recommendation set is different from the sum of their satisfaction with the individual items with the set and the approach to collecting item ratings from the user should be tailored to the algorithm being used. Finally, recommender systems within the movie domain seem to be more useful for “movie buffs ” rather than the “average movie watcher ” for whom recommending simply the most popular movies seems to be most appropriate. 1

Abstract. The role of context in our daily interaction with our environment has been studied in psychology, linguistics, artificial intelligence, information retrieval, and more recently, in pervasive/ubiquitous computing. However, context has been largely ignored in research into recommender systems specifically and personalization in general. In this paper we describe how context can be brought to bear on recommender systems. As a means for achieving this, we propose a fundamental shift in terms of how we model a user within a recommendation system: inspired by models of human memory developed in psychology, we distinguish between a user’s short term and long term memories, define a recommendation process that uses these two memories, using context-based retrieval cues to retrieve relevant preference information from long term memory and use it in conjunction with the information stored in short term memory for generating recommendations. We also describe implementations of recommender systems and personalization solutions based on this framework and show how this results in an increase in recommendation quality. 1

Abstract. There are inherent problems with evaluating the accuracy of recommender systems. Commonly-used metrics for recommender systems depend on the number of recommendations produced and the number of hidden items withheld, making it difficult to directly compare one system with another. In this paper we compare recommender algorithms using two datasets; the standard MovieLens set and an e-commerce dataset that has implicit ratings based on browsing behaviour. We introduce a measure that aids in the comparison and show how to compare results with baseline predictions based on random recommendation selections.

Abstract. Nowadays web users have clearly expressed their wishes to receive and interact with personalized services directly. However, existing approaches, largely syntactic content-based, fail to provide robust, accurate and useful personalized services to its users. Towards such an issue, the semantic web provides enabling technologies to annotate and match services ’ descriptions with a users’ features, interests and preferences, thus allowing for more efficient access to services and then information. The aim of our work, part of service personalization, is on automated instantiation of services which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? To this end, we exploit Description Logics reasoning through semantic matching to i) identify useful parts of a user profile that satisfy services requirements (i.e., input parameters) and ii) compute the description required by a service to be executed but not provided by the profile. Finally, the scalability of our approach has been evaluated through its integration in the service consumption of the EC-funded project SOA4All.

Mobile web applications refer to web applications on mobile devices, aimed at personalizing, integrating, and discovering mobile contents in user contexts. This thesis presents a comprehensive study of mobile web applications by proposing a new taxonomy for mobile web applications, and conducting a business analysis in the field of mobile web applications. The thesis reviews the current surrounding environment for mobile web applications, namely, web 2.0 and 3.0, wireless communication technology, and Smartphone platform. The recent entry and success of Apple’s iPhone greatly enhanced the public awareness of the Smartphone technology. Google’s release of open-source Android platform and T-Mobile’s deployment of Android-powered “Dream ” Smartphone not only intensify the competition among suppliers, but also provide an open-source foundation for mobile web applications. This thesis introduces a