On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this paper, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they expect from the system. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. Besides, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists.We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing the users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” data sets to compare our recommendation results with some other standard baseline methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage and aggregate diversity, while avoiding any accuracy loss

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this paper, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they expect from the system. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. Besides, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists.We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing the users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” data sets to compare our recommendation results with some other standard baseline methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage and aggregate diversity, while avoiding any accuracy loss

Personalised service discovery in mobile environments

In recent years, some trends have emerged that pertain both to mobile devices and the Web. On one side, mobile devices have transitioned from being simple wireless phones to become ubiquitous Web-enabled users' companions. On the other side, the Web has evolved from an online one-size-fits-all collection of interlinked documents to become an open platform of personalised services and content. It will not be long before these trends will converge and create a Seamless Web: an integrated environment where, besides traditional services delivered by powerful server machines accessible via wide area networks, new services and content will be offered by users to users via their portable devices. As a result, mobile users will soon be exposed - in addition to traditional "on-line" Web services/content - to a parallel universe of pervasive "off-line" services provided by devices in their surroundings. Such circumstances will raise new challenges when it comes to selecting the services to rely on, that will require solutions grounded on the characteristics of mobile environments. Two aspects will require particular attention: first, users will have access to a countless multitude of services impossible to explore; they will need assistance to identify, among this multitude, those services they are most likely to enjoy. Secondly, if today's services (and their providers) are always-on, `static' and aiming at Five 9s availability, tomorrow's pervasive services will be mobile (as devices move), fine-grained, increasingly composite (to provide richer functionalities) and so more unreliable by nature. Our research tackles the problem of service discovery in pervasive environments in two ways: on one hand, we support personalised discovery by means of a mobile recommender system, easing the discovery of pervasive services appealing to end-users. On the other hand, we enable reliable discovery, by reasoning on the composite nature of pervasive services and the physical availability of their component providers. Overall, we provide a discovery method that enables 'better' pervasive services, where by 'better' we mean both `more interesting' to the user and 'more reliable'