Towards Collaborative Travel Recommender Systems

Collaborative filtering (CF) based recommender systems have been proven to be a promising solution to the problem of information overload. Such systems provide personalized recommendations to users based on their previously expressed preferences and that of other similar users. In the past decade, they have been successfully applied in various domains, such as the recommendation of books and movies, where items are simple, independent and single units. When applied in the tourism domain, however, CF falls short due to the simplicity of existing techniques and complexity of tourism products. In view of this, a study was carried out to review the research problems and opportunities. This paper details the results of the study, which includes a review on the recent developments in CF as well as recommender systems in tourism, and suggests future research directions for personalized recommendation of tourist destinations and products

The Effects of Singular Value Decomposition on Collaborative Filtering

As the information on the web increases exponentially, so do the efforts to automatically filter out useless content and to search for interesting content. Through both explicit and implicit actions, users define where their interests lie. Recent efforts have tried to group similar users together in order to better use this data to provide the best overall filtering capabilities to everyone. This thesis discusses ways in which linear algebra, specifically the singular value decomposition, can be used to augment these filtering capabilities to provide better user feedback. The goal is to modify the way users are compared with one another, so that we can more efficiently predict similar users. Using data collected from the PhDs.org website, we tested our hypothesis on both explicit web page ratings and implicit visits data

Learning users' interests by quality classification in market-based recommender systems

Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the users’ ratings of their suggestions. Moreover, we have theoretically shown how our system incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the users’ interests for these different levels, and then adapt its bidding behaviour for the various levels accordingly. To this end, in this paper we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations