1,830 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations
The success of neural network embeddings has entailed a renewed interest in
using knowledge graphs for a wide variety of machine learning and information
retrieval tasks. In particular, current recommendation methods based on graph
embeddings have shown state-of-the-art performance. These methods commonly
encode latent rating patterns and content features. Different from previous
work, in this paper, we propose to exploit embeddings extracted from graphs
that combine information from ratings and aspect-based opinions expressed in
textual reviews. We then adapt and evaluate state-of-the-art graph embedding
techniques over graphs generated from Amazon and Yelp reviews on six domains,
outperforming baseline recommenders. Our approach has the advantage of
providing explanations which leverage aspect-based opinions given by users
about recommended items. Furthermore, we also provide examples of the
applicability of recommendations utilizing aspect opinions as explanations in a
visualization dashboard, which allows obtaining information about the most and
least liked aspects of similar users obtained from the embeddings of an input
graph
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but
more importantly to generate explanations of why particular decisions are made.
This can be achieved by explicit KG reasoning, where a model starts from a user
node, sequentially determines the next step, and walks towards an item node of
potential interest to the user. However, this is challenging due to the huge
search space, unknown destination, and sparse signals over the KG, so
informative and effective guidance is needed to achieve a satisfactory
recommendation quality. To this end, we propose a CoArse-to-FinE neural
symbolic reasoning approach (CAFE). It first generates user profiles as coarse
sketches of user behaviors, which subsequently guide a path-finding process to
derive reasoning paths for recommendations as fine-grained predictions. User
profiles can capture prominent user behaviors from the history, and provide
valuable signals about which kinds of path patterns are more likely to lead to
potential items of interest for the user. To better exploit the user profiles,
an improved path-finding algorithm called Profile-guided Path Reasoning (PPR)
is also developed, which leverages an inventory of neural symbolic reasoning
modules to effectively and efficiently find a batch of paths over a large-scale
KG. We extensively experiment on four real-world benchmarks and observe
substantial gains in the recommendation performance compared with
state-of-the-art methods.Comment: Accepted in CIKM 202
Dual Preference Distribution Learning for Item Recommendation
Recommender systems can automatically recommend users with items that they
probably like. The goal of them is to model the user-item interaction by
effectively representing the users and items. Existing methods have primarily
learned the user's preferences and item's features with vectorized embeddings,
and modeled the user's general preferences to items by the interaction of them.
In fact, users have their specific preferences to item attributes and different
preferences are usually related. Therefore, exploring the fine-grained
preferences as well as modeling the relationships among user's different
preferences could improve the recommendation performance. Toward this end, we
propose a dual preference distribution learning framework (DUPLE), which aims
to jointly learn a general preference distribution and a specific preference
distribution for a given user, where the former corresponds to the user's
general preference to items and the latter refers to the user's specific
preference to item attributes. Notably, the mean vector of each Gaussian
distribution can capture the user's preferences, and the covariance matrix can
learn their relationship. Moreover, we can summarize a preferred attribute
profile for each user, depicting his/her preferred item attributes. We then can
provide the explanation for each recommended item by checking the overlap
between its attributes and the user's preferred attribute profile. Extensive
quantitative and qualitative experiments on six public datasets demonstrate the
effectiveness and explainability of the DUPLE method.Comment: 23 pages, 7 figures. This manuscript has been accepted by ACM
Transactions on Information System
Rating and aspect-based opinion graph embeddings for explainable recommendations
The success of neural network embeddings has entailed a renewed interest in
using knowledge graphs for a wide variety of machine learning and information
retrieval tasks. In particular, recent recommendation methods based on graph
embeddings have shown state-of-the-art performance. In general, these methods
encode latent rating patterns and content features. Differently from previous
work, in this paper, we propose to exploit embeddings extracted from graphs
that combine information from ratings and aspect-based opinions expressed in
textual reviews. We then adapt and evaluate state-of-the-art graph embedding
techniques over graphs generated from Amazon and Yelp reviews on six domains,
outperforming baseline recommenders. Additionally, our method has the advantage
of providing explanations that involve the coverage of aspect-based opinions
given by users about recommended items.Comment: arXiv admin note: substantial text overlap with arXiv:2107.0322
Faithful Path Language Modelling for Explainable Recommendation over Knowledge Graph
Path reasoning methods over knowledge graphs have gained popularity for their
potential to improve transparency in recommender systems. However, the
resulting models still rely on pre-trained knowledge graph embeddings, fail to
fully exploit the interdependence between entities and relations in the KG for
recommendation, and may generate inaccurate explanations. In this paper, we
introduce PEARLM, a novel approach that efficiently captures user behaviour and
product-side knowledge through language modelling. With our approach, knowledge
graph embeddings are directly learned from paths over the KG by the language
model, which also unifies entities and relations in the same optimisation
space. Constraints on the sequence decoding additionally guarantee path
faithfulness with respect to the KG. Experiments on two datasets show the
effectiveness of our approach compared to state-of-the-art baselines. Source
code and datasets: AVAILABLE AFTER GETTING ACCEPTED
Explainability in Music Recommender Systems
The most common way to listen to recorded music nowadays is via streaming
platforms which provide access to tens of millions of tracks. To assist users
in effectively browsing these large catalogs, the integration of Music
Recommender Systems (MRSs) has become essential. Current real-world MRSs are
often quite complex and optimized for recommendation accuracy. They combine
several building blocks based on collaborative filtering and content-based
recommendation. This complexity can hinder the ability to explain
recommendations to end users, which is particularly important for
recommendations perceived as unexpected or inappropriate. While pure
recommendation performance often correlates with user satisfaction,
explainability has a positive impact on other factors such as trust and
forgiveness, which are ultimately essential to maintain user loyalty.
In this article, we discuss how explainability can be addressed in the
context of MRSs. We provide perspectives on how explainability could improve
music recommendation algorithms and enhance user experience. First, we review
common dimensions and goals of recommenders' explainability and in general of
eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which
these apply -- or need to be adapted -- to the specific characteristics of
music consumption and recommendation. Then, we show how explainability
components can be integrated within a MRS and in what form explanations can be
provided. Since the evaluation of explanation quality is decoupled from pure
accuracy-based evaluation criteria, we also discuss requirements and strategies
for evaluating explanations of music recommendations. Finally, we describe the
current challenges for introducing explainability within a large-scale
industrial music recommender system and provide research perspectives.Comment: To appear in AI Magazine, Special Topic on Recommender Systems 202
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