11 research outputs found
Text-based user-kNN:measuring user similarity based on text reviews
This article reports on a modification of the user-kNN algorithm that measures the similarity between users based on the similarity of text reviews, instead of ratings. We investigate the performance of text semantic similarity measures and we evaluate our text-based user-kNN approach by comparing it to a range of ratings-based approaches in a ratings prediction task. We do so by using datasets from two different domains: movies from RottenTomatoes and Audio CDs from Amazon Products. Our results show that the text-based userkNN algorithm performs significantly better than the ratings-based approaches in terms of accuracy measured using RMSE
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
Although latent factor models (e.g., matrix factorization) achieve good
accuracy in rating prediction, they suffer from several problems including
cold-start, non-transparency, and suboptimal recommendation for local users or
items. In this paper, we employ textual review information with ratings to
tackle these limitations. Firstly, we apply a proposed aspect-aware topic model
(ATM) on the review text to model user preferences and item features from
different aspects, and estimate the aspect importance of a user towards an
item. The aspect importance is then integrated into a novel aspect-aware latent
factor model (ALFM), which learns user's and item's latent factors based on
ratings. In particular, ALFM introduces a weighted matrix to associate those
latent factors with the same set of aspects discovered by ATM, such that the
latent factors could be used to estimate aspect ratings. Finally, the overall
rating is computed via a linear combination of the aspect ratings, which are
weighted by the corresponding aspect importance. To this end, our model could
alleviate the data sparsity problem and gain good interpretability for
recommendation. Besides, an aspect rating is weighted by an aspect importance,
which is dependent on the targeted user's preferences and targeted item's
features. Therefore, it is expected that the proposed method can model a user's
preferences on an item more accurately for each user-item pair locally.
Comprehensive experimental studies have been conducted on 19 datasets from
Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves
significant improvement compared with strong baseline methods, especially for
users with only few ratings. Moreover, our model could interpret the
recommendation results in depth.Comment: This paper has been accepted by the WWW 2018 Conferenc
A sentiment-based item description approach for kNN collaborative filtering
In this paper, we propose an approach based on sentiment analysis to describe items in a neighborhood-based collaborative filtering model. We use unstructured users' reviews to produce a vector-based representation that considers the overall sentiment of those reviews towards specific features. We propose and compare two different techniques to obtain and score such features from textual content, namely term-based and aspect-based feature extraction. Finally, our proposal is compared against structured metadata under the same recommendation algorithm, whose results show a significant improvement over the baselines.FAPESP (process numbers 2013/10756-5, and 2013/22547-1
A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
In popular applications such as e-commerce sites and social media, users
provide online reviews giving personal opinions about a wide array of items, such
as products, services and people. These reviews are usually in the form of free text,
and represent a rich source of information about the users’ preferences. Among the
information elements that can be extracted from reviews, opinions about particular
item aspects (i.e., characteristics, attributes or components) have been shown to be
effective for user modeling and personalized recommendation. In this paper, we investigate
the aspect-based recommendation problem by separately addressing three
tasks, namely identifying references to item aspects in user reviews, classifying the
sentiment orientation of the opinions about such aspects in the reviews, and exploiting
the extracted aspect opinion information to provide enhanced recommendations. Differently
to previous work, we integrate and empirically evaluate several state-of-the-art
and novel methods for each of the above tasks. We conduct extensive experiments
on standard datasets and several domains, analyzing distinct recommendation quality
metrics and characteristics of the datasets, domains and extracted aspects. As a result
of our investigation, we not only derive conclusions about which combination of methods
is most appropriate according to the above issues, but also provide a number of
valuable resources for opinion mining and recommendation purposes, such as domain
aspect vocabularies and domain-dependent, aspect-level lexiconsThis work was supported by the Spanish Ministry of Economy, Industry and Competitiveness
(TIN2016-80630-P)
Explainable Recommendation: Theory and Applications
Although personalized recommendation has been investigated for decades, the
wide adoption of Latent Factor Models (LFM) has made the explainability of
recommendations a critical issue to both the research community and practical
application of recommender systems. For example, in many practical systems the
algorithm just provides a personalized item recommendation list to the users,
without persuasive personalized explanation about why such an item is
recommended while another is not. Unexplainable recommendations introduce
negative effects to the trustworthiness of recommender systems, and thus affect
the effectiveness of recommendation engines. In this work, we investigate
explainable recommendation in aspects of data explainability, model
explainability, and result explainability, and the main contributions are as
follows:
1. Data Explainability: We propose Localized Matrix Factorization (LMF)
framework based Bordered Block Diagonal Form (BBDF) matrices, and further
applied this technique for parallelized matrix factorization.
2. Model Explainability: We propose Explicit Factor Models (EFM) based on
phrase-level sentiment analysis, as well as dynamic user preference modeling
based on time series analysis. In this work, we extract product features and
user opinions towards different features from large-scale user textual reviews
based on phrase-level sentiment analysis techniques, and introduce the EFM
approach for explainable model learning and recommendation.
3. Economic Explainability: We propose the Total Surplus Maximization (TSM)
framework for personalized recommendation, as well as the model specification
in different types of online applications. Based on basic economic concepts, we
provide the definitions of utility, cost, and surplus in the application
scenario of Web services, and propose the general framework of web total
surplus calculation and maximization.Comment: 169 pages, in Chinese, 3 main research chapter