X Qian, H Feng, G Zhao, T Mei - IEEE transactions on …, 2013 - ieeexplore.ieee.org
With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like …
In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work …
Exponential growth of information generated by online social networks demands effective recommender systems to give useful results. Traditional techniques become unqualified …
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list- wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of items is …
DPD Rajendran, RP Sundarraj - International Journal of Information …, 2021 - Elsevier
Personalizing user experience in recommender systems is possible when there is sufficient information about the user. But when new users join the system, the unavailability of …
Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by …
With the increasing popularity of location-based services, we have accumulated a lot of location data on the Web. In this paper, we are interested in answering two popular location …
G Zhao, X Qian, X Xie - IEEE transactions on multimedia, 2016 - ieeexplore.ieee.org
With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast …
M Jiang, P Cui, X Chen, F Wang… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Recommender systems can suffer from data sparsity and cold start issues. However, social networks, which enable users to build relationships and create different types of items …