1,293 research outputs found
Top-N Recommendation on Graphs
Recommender systems play an increasingly important role in online
applications to help users find what they need or prefer. Collaborative
filtering algorithms that generate predictions by analyzing the user-item
rating matrix perform poorly when the matrix is sparse. To alleviate this
problem, this paper proposes a simple recommendation algorithm that fully
exploits the similarity information among users and items and intrinsic
structural information of the user-item matrix. The proposed method constructs
a new representation which preserves affinity and structure information in the
user-item rating matrix and then performs recommendation task. To capture
proximity information about users and items, two graphs are constructed.
Manifold learning idea is used to constrain the new representation to be smooth
on these graphs, so as to enforce users and item proximities. Our model is
formulated as a convex optimization problem, for which we need to solve the
well-known Sylvester equation only. We carry out extensive empirical
evaluations on six benchmark datasets to show the effectiveness of this
approach.Comment: CIKM 201
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop,
co-located with ICDAR 2017 in Kyoto on November 10, 201
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
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