This paper proposes LARS*, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS*, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and ...

Users often have difficulties to use large-scale information systems efficiently because of their complexity. Additionally, these systems might be context dependent. If these context dependencies are taken into account during the system’s run-time phase, the most appropriate functionality might be provided to users in the form of recommendations for each context situation. The paper proposes to...

In this document, we summarize my PhD thesis goals and the progression in 2014/2015. The principal goal of my PhD thesis is to describe an architecture to design context social recommender systems. Finally, we explain all goals that we will try to achieve during my PhD studies.

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link with corresponding resources, therefore leading to static profile recommendation. In this article, we have proposed new social expansion model (STEM) generate dynamic improve recommendation performance. Instead simply including most rel...

Trust-aware recommender system (TARS) recommends ratings based on user trust. It greatly improves the conventional collaborative filtering by providing reliable recommendations when dealing with the data sparseness problem. One basic research issue of TARS is to improve the recommending efficiency, in which the key point is to find sufficient number of recommenders efficiently for active users....

As mobile devices, especially smartphones, become more and more popular, the number of mobile applications increases dramatically. Thoughmobile applications provide users convenience and entertainment, they have potential threat to violate users’ privacy and security. In order to decrease the risk of violation, we propose a risk and similarity aware application recommender system, which recomme...

Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation...

Recommender systems are similar to an information filtering system that helps identify items best satisfy the users’ demands based on their preference profiles. Context-aware recommender (CARSs) and multi-criteria (MCRSs) extensions of traditional systems. CARSs have integrated additional contextual such as time, place, so for providing better recommendations. However, majority use ratings a un...

Recommender systems support users in €nding relevant items in overloaded information spaces. Researchers and practitioners have proposed many di‚erent collaborative €ltering algorithms for different information scenarios, domains and contexts. One of the laŠer, are time-aware recommender methods that consider temporal dynamics in the users’ interests in certain items, topics, etc. While there i...