CiteULike: Group: Adaptive-Web - with tag collaborative-filtering

Recommending related papers based on digital library access records

Stefan Pohl — 2008-04-17T15:53:00-00:00

(2007), pp. 417-418.

Social information filtering: algorithms for automating “word of mouth”

Upendra Shardanand — 2005-08-10T18:07:44-00:00

(1995), pp. 210-217.

Information retrieval based on collaborative filtering with latent interest semantic map

Noriaki Kawamae — 2008-01-23T20:13:05-00:00

(2005), pp. 618-623.

A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation

Degemmis — 2007-07-10T06:33:10-00:00

User Modeling and User-Adapted Interaction, Vol. 17, No. 3. (July 2007), pp. 217-255.

Getting to know you: learning new user preferences in recommender systems

Al Rashid — 2007-05-17T18:34:08-00:00

(2002), pp. 127-134.

Combining eye movements and collaborative filtering for proactive information retrieval

Kai Puolamäki — 2007-10-08T18:48:24-00:00

(2005), pp. 146-153.

Personalized recommendation driven by information flow

Xiaodan Song — 2006-12-12T04:40:41-00:00

(2006), pp. 509-516.

We propose that the information access behavior of a group of people can be modeled as an information flow issue, in which people intentionally or unintentionally influence and inspire each other, thus creating an interest in retrieving or getting a specific kind of information or product. Information flow models how information is propagated in a social network. It can be a real social network where interactions between people reside; it can be, moreover, a virtual social network in that people only influence each other unintentionally, for instance, through collaborative filtering. We leverage users' access patterns to model information flow and generate effective personalized recommendations. First, an early adoption based information flow (EABIF) network describes the influential relationships between people. Second, based on the fact that adoption is typically category specific, we propose a topic-sensitive EABIF (TEABIF) network, in which access patterns are clustered with respect to the categories. Once an item has been accessed by early adopters, personalized recommendations are achieved by estimating whom the information will be propagated to with high probabilities. In our experiments with an online document recommendation system, the results demonstrate that the EABIF and the TEABIF can respectively achieve an improved (precision, recall) of (91.0%, 87.1%) and (108.5%, 112.8%) compared to traditional collaborative filtering, given an early adopter exists.

Pointing the way: active collaborative filtering

David Maltz — 2006-10-11T17:19:40-00:00

(1995), pp. 202-209.

Evaluating collaborative filtering recommender systems

Jonathan Herlocker — 2005-04-27T17:40:41-00:00

ACM Trans. Inf. Syst., Vol. 22, No. 1. (January 2004), pp. 5-53.

E-Commerce Recommendation Applications

Ben Schafer — 2005-06-30T14:38:17-00:00

Data Mining and Knowledge Discovery, Vol. 5, No. 1 - 2. (January 2001), pp. 115-153.

Group Lens: An open architecture for collaborative filtering of netnews

Paul Resnick — 2006-05-10T14:05:12-00:00

(1994), pp. 175-186.

Erklärung des k-nearest neighbour Algorithmus aus der Bewertungsmatrix zur Vorhersage von Item-Bewertungen. Weiterhin kurze Ausführungen zur einheitlichen Präsentation von Bewertungen und Artikeln in den Newsreader Programmen.