Aggregated data in real world recommender applications often feature fat-tailed distributions of the number of times individual items have been rated or favored. We propose a model to simulate such data. The model is mainly based on social interactions and opinion formation taking place on a complex network with a given topology. A threshold mechanism is used to govern the decision making proce...

The evaluation of recommender systems is key to the successful application of recommender systems in practice. However, recommender-systems evaluation has received too little attention in the recommender-system community, in particular in the community of research-paper recommender systems. In this paper, we examine and discuss the appropriateness of different evaluation methods, i.e. offline e...

Conversational recommender systems help to guide users through a product-space towards a particular product that meets their specific requirements. During the course of a “conversation” with the user the recommender system will suggest certain products and use feedback from the user to refine future suggestions. Critiquing has proven to be a powerful and popular form of feedback. Critiques allo...

This thesis consists of three papers on recommender systems. The first paper addresses the problem of making decentralized recommendations using a peer-to-peer architecture. Collaborating recommender agents are connected into a network of neighbors that exchange user recommendations to find new items to recommend. We achieved a performance comparable to a centralized system. The second paper de...

One approach to distributed recommender systems is to have users sample products at random and randomly query one another for the best item they have found. We have been considering refinements to this approach that take advantage of a communication network; users may share information only with their immediate neighbors, who either by design or by nature may have common interests. In the “mail...

The objective of an information retrieval (IR) system is to retrieve relevant items which meet a user information need. There is currently significant interest in personalized IR which seeks to improve IR effectiveness by incorporating a model of the user’s interests. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. In o...

Recommender systems use people’s opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users’ opinions of the items. If opinions are influ...

Recommender system plays an important role in many e-commerce services, such as in Rakuten. In this paper, we focus on the item-to-item recommender and the user-to-item recommenders, which are two most widely used functions in online services for presenting relevant items given an item, or a particular user. We use a large amount of log data from one of Rakuten markets, and apply distributed re...

One of the major challenges in Recommender Systems is how to predict users’ preferences in a group context. There are situations in which a user could be recommended an item appropriated for one of their groups, but the same item may not be suitable when interacting with another group. There are situations in which a user could be recommended an item appropriated for one of their groups (e.g. p...