RECSYS
2010
ACM
13 years 10 months ago
2010
ACM
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 item...
RECSYS
2010
ACM
13 years 11 months ago
2010
ACM
Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor, given the rewards that can be derived from posting false or biase...
RECSYS
2010
ACM
13 years 11 months ago
2010
ACM
The reciprocal recommender is a class of recommender system that is important for several tasks where people are both the subjects and objects of the recommendation. Some examples...
RECSYS
2010
ACM
14 years 27 days ago
2010
ACM
RECSYS
2010
ACM
14 years 28 days ago
2010
ACM
Since the development of the comparably simple neighborhood-based methods in the 1990s, a plethora of techniques has been developed to improve various aspects of collaborative fil...
RECSYS
2010
ACM
14 years 28 days ago
2010
ACM
The so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated ...
RECSYS
2010
ACM
14 years 28 days ago
2010
ACM
Recommendation systems suggest items based on user preferences. Collaborative filtering is a popular approach in which recommending is based on the rating history of the system. O...
RECSYS
2010
ACM
14 years 28 days ago
2010
ACM
Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links amongst users, the phenomenon of users associating th...
RECSYS
2010
ACM
14 years 28 days ago
2010
ACM
Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Fac...
RECSYS
2010
ACM
14 years 28 days ago
2010
ACM
We present the design, implementation and evaluation of a new geotagging service, Gloe, that makes it easy to find, rate and recommend arbitrary on-line content in a mobile settin...