808 research outputs found
Collaborative personalised dynamic faceted search
Information retrieval systems are facing challenges due to the overwhelming volume of available information online. It leads to the need of search features that have the capability to provide relevant information for searchers. Dynamic faceted search has been one of the potential tools to provide a list of multiple facets for searchers to filter their contents. However, being a dynamic system, some irrelevant or unimportant facets could be produced. To develop an effective dynamic faceted search, personalised facet selection is an important mechanism to create an appropriate personalised facet list. Most current systems have derived the searchers' interests from their own profiles. However, interests from the past may not be adequate to predict current interest due to human information-seeking behaviour. Incorporating current interests from other people's opinions to predict the interests of individual person is an alternative way to develop personalisation which is called Collaborative approach. This research aims to investigate the incorporation of a Collaborative approach to personalise facet selection. This study introduces the Artificial Neural Network (ANN)-based collaborative personalisation architecture framework and Relation-aware Collaborative AutoEncoder model (RCAE) with embedding methodology for modelling and predicting the interests in multiple facets. The study showed that incorporating collaborative approach into the proposed framework for facet selection is capable to enhance the performance of personalisation model in facet selection in comparison to the state-of-the-art techniques
An improved model for trust-aware recommender systems based on multi-faceted trust
As customers enjoy the convenience of online shopping today, they face the problem of selecting from hundreds of thousands of products. Recommender systems, which make recommendations by matching products to customers based on the features of the products and the purchasing history of customers, are increasingly being incorporated into e-commerce websites. Collaborative filtering is a major approach to design algorithms for these systems. Much research has been directed toward enhancing the performance of recommender systems by considering various psychological and behavioural factors affecting the behaviour of users, e.g. trust and emotion.
While e-commerce firms are keen to exploit information on social trust available on social networks to improve their services, conventional trust-aware collaborative filtering does not consider the multi-facets of social trust. In this research, we assume that a consumer tends to trust different people for recommendations on different types of product. For example, a user trusts a certain reviewer on popular items but may not place as much trust on the same reviewer on unpopular items. Furthermore, this thesis postulates that if we, as online shoppers, choose to establish trust on an individual while we ourselves are reviewing certain products, we value this individual’s opinions on these products and we most likely will value his/her opinions on similar products in future.
Based on the above assumptions, this thesis proposes a new collaborative filtering algorithm for deriving multi-faceted trust based on trust establishment time. Experimental results based on historical data from Epinions show that the new algorithm can perform better in terms of accuracy when compared with conventional algorithms
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Modern recommender systems lie at the heart of complex ecosystems that couple
the behavior of users, content providers, advertisers, and other actors.
Despite this, the focus of the majority of recommender research -- and most
practical recommenders of any import -- is on the local, myopic optimization of
the recommendations made to individual users. This comes at a significant cost
to the long-term utility that recommenders could generate for its users. We
argue that explicitly modeling the incentives and behaviors of all actors in
the system -- and the interactions among them induced by the recommender's
policy -- is strictly necessary if one is to maximize the value the system
brings to these actors and improve overall ecosystem "health". Doing so
requires: optimization over long horizons using techniques such as
reinforcement learning; making inevitable tradeoffs in the utility that can be
generated for different actors using the methods of social choice; reducing
information asymmetry, while accounting for incentives and strategic behavior,
using the tools of mechanism design; better modeling of both user and
item-provider behaviors by incorporating notions from behavioral economics and
psychology; and exploiting recent advances in generative and foundation models
to make these mechanisms interpretable and actionable. We propose a conceptual
framework that encompasses these elements, and articulate a number of research
challenges that emerge at the intersection of these different disciplines
Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
In many industrial applications like online advertising and recommendation
systems, diverse and accurate user profiles can greatly help improve
personalization. For building user profiles, deep learning is widely used to
mine expressive tags to describe users' preferences from their historical
actions. For example, tags mined from users' click-action history can represent
the categories of ads that users are interested in, and they are likely to
continue being clicked in the future. Traditional solutions usually introduce
multiple independent Two-Tower models to mine tags from different actions,
e.g., click, conversion. However, the models cannot learn complementarily and
support effective training for data-sparse actions. Besides, limited by the
lack of information fusion between the two towers, the model learning is
insufficient to represent users' preferences on various topics well. This paper
introduces a novel multi-task model called Mixture of Virtual-Kernel Experts
(MVKE) to learn multiple topic-related user preferences based on different
actions unitedly. In MVKE, we propose a concept of Virtual-Kernel Expert, which
focuses on modeling one particular facet of the user's preference, and all of
them learn coordinately. Besides, the gate-based structure used in MVKE builds
an information fusion bridge between two towers, improving the model's
capability much and maintaining high efficiency. We apply the model in Tencent
Advertising System, where both online and offline evaluations show that our
method has a significant improvement compared with the existing ones and brings
about an obvious lift to actual advertising revenue.Comment: 10 pages, under revie
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
As a step beyond traditional personalized recommendation, group
recommendation is the task of suggesting items that can satisfy a group of
users. In group recommendation, the core is to design preference aggregation
functions to obtain a quality summary of all group members' preferences. Such
user and group preferences are commonly represented as points in the vector
space (i.e., embeddings), where multiple user embeddings are compressed into
one to facilitate ranking for group-item pairs. However, the resulted group
representations, as points, lack adequate flexibility and capacity to account
for the multi-faceted user preferences. Also, the point embedding-based
preference aggregation is a less faithful reflection of a group's
decision-making process, where all users have to agree on a certain value in
each embedding dimension instead of a negotiable interval. In this paper, we
propose a novel representation of groups via the notion of hypercubes, which
are subspaces containing innumerable points in the vector space. Specifically,
we design the hypercube recommender (CubeRec) to adaptively learn group
hypercubes from user embeddings with minimal information loss during preference
aggregation, and to leverage a revamped distance metric to measure the affinity
between group hypercubes and item points. Moreover, to counteract the
long-standing issue of data sparsity in group recommendation, we make full use
of the geometric expressiveness of hypercubes and innovatively incorporate
self-supervision by intersecting two groups. Experiments on four real-world
datasets have validated the superiority of CubeRec over state-of-the-art
baselines.Comment: To appear in SIGIR'2
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