[图书][B] Recommender systems
CC Aggarwal - 2016 - Springer
“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …
Personalized recommendation combining user interest and social circle
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 …
experiences, such as ratings, reviews, and blogs. The new factors of social network like …
Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering
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 …
relevance data, when only a few (k) items are recommended to individual users. Past work …
Social contextual recommendation
Exponential growth of information generated by online social networks demands effective
recommender systems to give useful results. Traditional techniques become unqualified …
recommender systems to give useful results. Traditional techniques become unqualified …
List-wise learning to rank with matrix factorization for collaborative filtering
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 …
wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of items is …
[HTML][HTML] Using topic models with browsing history in hybrid collaborative filtering recommender system: Experiments with user ratings
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 …
information about the user. But when new users join the system, the unavailability of …
Enhancing collaborative filtering by user interest expansion via personalized ranking
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 …
understanding their past behaviors. In these systems, the user behaviors are influenced by …
Towards mobile intelligence: Learning from GPS history data for collaborative recommendation
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 …
location data on the Web. In this paper, we are interested in answering two popular location …
User-service rating prediction by exploring social users' rating behaviors
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 …
doing with friends across various social networking platforms. Nowadays, we have a vast …
Social recommendation with cross-domain transferable knowledge
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 …
networks, which enable users to build relationships and create different types of items …