3 research outputs found
Reliable graph-based collaborative ranking
GRank is a recent graph-based recommendation approach the uses a novel
heterogeneous information network to model users' priorities and analyze it to
directly infer a recommendation list. Unfortunately, GRank neglects the
semantics behind different types of paths in the network and during the
process, it may use unreliable paths that are inconsistent with the general
idea of similarity in neighborhood collaborative ranking. That negligence
undermines the reliability of the recommendation list generated by GRank. This
paper seeks to present a novel framework for reliable graph-based collaborative
ranking, called ReGRank, that ranks items based on reliable recommendation
paths that are in harmony with the semantics behind different approaches in
neighborhood collaborative ranking. To our knowledge, ReGRank is the first
unified framework for neighborhood collaborative ranking that in addition to
traditional user-based collaborative ranking, can also be adapted for
preference-based and representative-based collaborative ranking as well.
Experimental results show that ReGRank significantly improves the state-of-the
art neighborhood and graph-based collaborative ranking algorithms.Comment: 30 pages, 9 figures, 3 Table
GEMRank: Global Entity Embedding For Collaborative Filtering
Recently, word embedding algorithms have been applied to map the entities of
recommender systems, such as users and items, to new feature spaces using
textual element-context relations among them. Unlike many other domains, this
approach has not achieved a desired performance in collaborative filtering
problems, probably due to unavailability of appropriate textual data. In this
paper we propose a new recommendation framework, called GEMRank that can be
applied when the user-item matrix is the sole available souce of information.
It uses the concept of profile co-occurrence for defining relations among
entities and applies a factorization method for embedding the users and items.
GEMRank then feeds the extracted representations to a neural network model to
predict user-item like/dislike relations which the final recommendations are
made based on. We evaluated GEMRank in an extensive set of experiments against
state of the art recommendation methods. The results show that GEMRank
significantly outperforms the baseline algorithms in a variety of data sets
with different degrees of density
Embedding Ranking-Oriented Recommender System Graphs
Graph-based recommender systems (GRSs) analyze the structural information in
the graphical representation of data to make better recommendations, especially
when the direct user-item relation data is sparse. Ranking-oriented GRSs that
form a major class of recommendation systems, mostly use the graphical
representation of preference (or rank) data for measuring node similarities,
from which they can infer a recommendation list using a neighborhood-based
mechanism. In this paper, we propose PGRec, a novel graph-based
ranking-oriented recommendation framework. PGRec models the preferences of the
users over items, by a novel graph structure called PrefGraph. This graph is
then exploited by an improved embedding approach, taking advantage of both
factorization and deep learning methods, to extract vectors representing users,
items, and preferences. The resulting embedding are then used for predicting
users' unknown pairwise preferences from which the final recommendation lists
are inferred. We have evaluated the performance of the proposed method against
the state of the art model-based and neighborhood-based recommendation methods,
and our experiments show that PGRec outperforms the baseline algorithms up to
3.2% in terms of NDCG@10 in different MovieLens datasets