1,333 research outputs found
Bootstrapped Personalized Popularity for Cold Start Recommender Systems
Recommender Systems are severely hampered by the well-known Cold Start problem, identified by the lack of information on new items and users. This has led to research efforts focused on data imputation and augmentation models as predominantly data pre-processing strategies, yet their improvement of cold-user performance is largely indirect and often comes at the price of a reduction in accuracy for warmer users. To address these limitations, we propose Bootstrapped Personalized Popularity (B2P), a novel framework that improves performance for cold users (directly) and cold items (implicitly) via popularity models personalized with item metadata. B2P is scalable to very large datasets and directly addresses the Cold Start problem, so it can complement existing Cold Start strategies. Experiments on a real-world dataset from the BBC iPlayer and a public dataset demonstrate that B2P (1) significantly improves cold-user performance, (2) boosts warm-user performance for bootstrapped models by lowering their training sparsity, and (3) improves total recommendation accuracy at a competitive diversity level relative to existing high-performing Collaborative Filtering models. We demonstrate that B2P is a powerful and scalable framework for strongly cold datasets
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
Collaborative filtering based recommender systems have proven to be extremely
successful in settings where user preference data on items is abundant.
However, collaborative filtering algorithms are hindered by their weakness
against the item cold-start problem and general lack of interpretability.
Ontology-based recommender systems exploit hierarchical organizations of users
and items to enhance browsing, recommendation, and profile construction. While
ontology-based approaches address the shortcomings of their collaborative
filtering counterparts, ontological organizations of items can be difficult to
obtain for items that mostly belong to the same category (e.g., television
series episodes). In this paper, we present an ontology-based recommender
system that integrates the knowledge represented in a large ontology of
literary themes to produce fiction content recommendations. The main novelty of
this work is an ontology-based method for computing similarities between items
and its integration with the classical Item-KNN (K-nearest neighbors)
algorithm. As a study case, we evaluated the proposed method against other
approaches by performing the classical rating prediction task on a collection
of Star Trek television series episodes in an item cold-start scenario. This
transverse evaluation provides insights into the utility of different
information resources and methods for the initial stages of recommender system
development. We found our proposed method to be a convenient alternative to
collaborative filtering approaches for collections of mostly similar items,
particularly when other content-based approaches are not applicable or
otherwise unavailable. Aside from the new methods, this paper contributes a
testbed for future research and an online framework to collaboratively extend
the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision
Bandits Warm-up Cold Recommender Systems
We address the cold start problem in recommendation systems assuming no
contextual information is available neither about users, nor items. We consider
the case in which we only have access to a set of ratings of items by users.
Most of the existing works consider a batch setting, and use cross-validation
to tune parameters. The classical method consists in minimizing the root mean
square error over a training subset of the ratings which provides a
factorization of the matrix of ratings, interpreted as a latent representation
of items and users. Our contribution in this paper is 5-fold. First, we
explicit the issues raised by this kind of batch setting for users or items
with very few ratings. Then, we propose an online setting closer to the actual
use of recommender systems; this setting is inspired by the bandit framework.
The proposed methodology can be used to turn any recommender system dataset
(such as Netflix, MovieLens,...) into a sequential dataset. Then, we explicit a
strong and insightful link between contextual bandit algorithms and matrix
factorization; this leads us to a new algorithm that tackles the
exploration/exploitation dilemma associated to the cold start problem in a
strikingly new perspective. Finally, experimental evidence confirm that our
algorithm is effective in dealing with the cold start problem on publicly
available datasets. Overall, the goal of this paper is to bridge the gap
between recommender systems based on matrix factorizations and those based on
contextual bandits
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word
embedding models in recommendation, we propose a novel Regularized
Multi-Embedding (RME) based recommendation model that simultaneously
encapsulates the following ideas via decomposition: (1) which items a user
likes, (2) which two users co-like the same items, (3) which two items users
often co-liked, and (4) which two items users often co-disliked. In
experimental validation, the RME outperforms competing state-of-the-art models
in both explicit and implicit feedback datasets, significantly improving
Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition,
under the cold-start scenario for users with the lowest number of interactions,
against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in
MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source
code are available at: https://github.com/thanhdtran/RME.git.Comment: CIKM 201
Deriving item features relevance from collaborative domain knowledge
An Item based recommender system works by computing a similarity between
items, which can exploit past user interactions (collaborative filtering) or
item features (content based filtering). Collaborative algorithms have been
proven to achieve better recommendation quality then content based algorithms
in a variety of scenarios, being more effective in modeling user behaviour.
However, they can not be applied when items have no interactions at all, i.e.
cold start items. Content based algorithms, which are applicable to cold start
items, often require a lot of feature engineering in order to generate useful
recommendations. This issue is specifically relevant as the content descriptors
become large and heterogeneous. The focus of this paper is on how to use a
collaborative models domain-specific knowledge to build a wrapper feature
weighting method which embeds collaborative knowledge in a content based
algorithm. We present a comparative study for different state of the art
algorithms and present a more general model. This machine learning approach to
feature weighting shows promising results and high flexibility
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