17,802 research outputs found
Sequential Recommendation Based on Objective and Subjective Features
Nowadays, sequential recommender systems are widely used in E-commerce fields to capture consumers’ dynamic preferences in short terms. Existing transformer-based recommendation models mainly consider consumer preference for the products and some related features, such as price. However, besides such objective features, some subjective features, such as consumers’ preference for product quality, also affect consumers’ purchase decisions. In this paper, we design a Sequential Recommender system based on Objective and Subjective features (SROS). We construct subjective features by using natural language processing to analyze online consumer reviews. Then we design a feature-level multi-head self-attention to explore the interactions between objective features and subjective features and capture consumers’ dynamic preferences for them among different purchases. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model
Comparision of Utility-Based Recommendation Methods
In World Wide Web environments, recommender systems are useful to reduce information overloading. A content-based recommender system recommends items according to their features. Vector Space Model (VSM) is a popular way to recommend items that are similar to those the user liked in the past. The main disadvantages of this content-based method are overspecialization and new user problems that incurred by incomplete information on user preferences. Therefore, to construct users\u27 complete preference profiles may enhance the effectiveness of recommender systems. Some utility function elicitation methods have been developed based on Multi-Attribute Utility Theory. Whether these utility-based methods are able to outperform the traditional VSM method for recommendations is investigated in this research. This research adopts the RBFN and SMARTER methods to construct users\u27 multi-attribute utility functions that represent their complete preferences. A laboratory experiment is conducted to compare the utility-based methods with the traditional VSM method in terms of recommendation accuracy, time expense, and user perceptions. The research results demonstrate that the VSM method is suitable to recommend items with mostly nominal attributes, and the SMARTER method is suitable to recommend items with mostly numerical attributes. The RBFN method has reliable accuracy and time expense in both recommendation contexts
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
Deep Causal Reasoning for Recommendations
Traditional recommender systems aim to estimate a user's rating to an item
based on observed ratings from the population. As with all observational
studies, hidden confounders, which are factors that affect both item exposures
and user ratings, lead to a systematic bias in the estimation. Consequently, a
new trend in recommender system research is to negate the influence of
confounders from a causal perspective. Observing that confounders in
recommendations are usually shared among items and are therefore multi-cause
confounders, we model the recommendation as a multi-cause multi-outcome (MCMO)
inference problem. Specifically, to remedy confounding bias, we estimate
user-specific latent variables that render the item exposures independent
Bernoulli trials. The generative distribution is parameterized by a DNN with
factorized logistic likelihood and the intractable posteriors are estimated by
variational inference. Controlling these factors as substitute confounders,
under mild assumptions, can eliminate the bias incurred by multi-cause
confounders. Furthermore, we show that MCMO modeling may lead to high variance
due to scarce observations associated with the high-dimensional causal space.
Fortunately, we theoretically demonstrate that introducing user features as
pre-treatment variables can substantially improve sample efficiency and
alleviate overfitting. Empirical studies on simulated and real-world datasets
show that the proposed deep causal recommender shows more robustness to
unobserved confounders than state-of-the-art causal recommenders. Codes and
datasets are released at https://github.com/yaochenzhu/deep-deconf
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
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