collaborative filtering

Collaborative filtering technology reveals a number of bottlenecks to be addressed in the face of current challenges.

A less obvious means of Web personalization is collaborative-filtering software that resides on a Web site and tracks users' movements.

Collaborative filtering works only as well as the data it has available, and humans produce noisy, low-quality data.

However, existing collaborative filtering algorithms do not consider the change of user interests.

All this means that there's a ceiling to how accurate collaborative filtering can get.

In this basis, we analysed challenges which collaborative filtering recommendation approach suffered from.

These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering.

Using collaborative filtering to generate recommendations is computationally expensive.

The traditional Collaborative Filtering has shortcoming as follows: accuracy, data sparse and cold-start.

Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering.

Very few systems now are combining metadata or content with collaborative filtering.

A privacy-preserving collaborative filtering algorithm based on non-negative matrix factorization (NMF) is presented.

The key to item-to-item collaborative filtering's scalability and performance is that it creates the expensive similar-items table offline.

In this paper, based on multi-attribute the recommendation to improve the collaborative filtering algorithms.

The key point to grasp about collaborative-filtering software is that it knows absolutely nothing about movies.

A collaborative filtering recommendation algorithm based on the item features model is proposed in this paper.

There are two basic principles involved in Collaborative Filtering.

How to ensure the quality of collaborative filtering algorithm has become a major problem.

Using a technique called collaborative filtering, they can find out your likely interests.

Non-lineal gradual forgetting collaborative filtering algorithm capable of adapting to users'drifting interest

Electronic Commerce; Personalized Recommendation; Collaborative Filtering; Dual Information Source; MAE(mean absolute error);

A Collaborative Filtering Recommendation Algorithm Incorporated with User Interest Change

A Preliminary Study about Optimization of Recommendations in Collaborative Filtering Algorithms

Combining Singular Value Decomposition and Neighbor-based Method in Collaborative Filtering

Research on Lineal Gradual Forgetting Collaborative Filtering Algorithm

Research on Application of Collaborative Filtering in Recommender Systems

Research on application of item-based collaborative filtering in recommender systems

An Efficient Collaborative Filtering Algorithm with User Hierarchy

Traditional collaborative filtering methods, unavoidably, are puzzled by the problem of "data-sparsity" ;

A Study on Item-Based Collaborative Filtering Algorithm Using Semantic Similarity

Research of Information Filtering Methods Based on Content-Based Filtering and Collaborative Filtering

Construction of Information Retrieval System Based on User Feedback and Collaborative Filtering

Collaborative Filtering Recommendation Based on Fuzzy Clustering and Item Smoothing

A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction

Collaborative Filtering Recommendation Based on Content and Item Rating Prediction

A New Algorithm of Solving the Sparsity of Matrix in Collaborative Filtering

Collaborative filtering recommendation approach based on CoP modeling

Collaborative Filtering Algorithm Based on Matrix Partition and Interest Variance

Self-adaptive test building algorithm based on Collaborative Filtering technique

Research on Recommendation Model Based on Supply and Demand Relation and Collaborative Filtering

Research on collaborative filtering algorithm based on item's attribute implicit rating

Model of Collaborative Filtering in E-commerce Recommender System Based on Interest Measure

A Survey of E-Commerce Recommendation System Based on Collaborative Filtering

CRM Active Marketing Model Based on Collaborative Filtering

A collaborative filtering algorithm based on classify

A New Model of User Navigation Prediction Based on Collaborative Filtering

Research of Agent collaborative filtering model based on user's feedback

Collaborative Filtering Algorithm based on Rough Set

A Kind of Adaptive Recommend System Based on Collaborative Filtering Technology