6,341 research outputs found
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)
A Social Framework for Set Recommendation in Group Recommender Systems
This research article presents a study about the background in Group Recommender Systems and how social factors are directly related to these applications. Some important group recommender systems in academia are described to exemplify their contribution in different domains. Besides, a framework that is intended to improve group recommender systems is proposed. The main idea of the framework is to enhance social cognition to help the group members agree and make a decision. Its structure includes a process where an influential group is detected among the target groups of people to recommend to. Social influence detection uses the knowledge behind online social connections and interactions. Trying to understand human behavior and ties among groups in a social network and how to use this to improve group recommender systems is considered the main challenge for future research. Combining this with the kind of item recommendation which involves a temporal sequence of ordered elements will present a novel and original path in Group Recommender Systems design.
 
Arabic Opinion Mining Using a Hybrid Recommender System Approach
Recommender systems nowadays are playing an important role in the delivery of
services and information to users. Sentiment analysis (also known as opinion
mining) is the process of determining the attitude of textual opinions, whether
they are positive, negative or neutral. Data sparsity is representing a big
issue for recommender systems because of the insufficiency of user rating or
absence of data about users or items. This research proposed a hybrid approach
combining sentiment analysis and recommender systems to tackle the problem of
data sparsity problems by predicting the rating of products from users reviews
using text mining and NLP techniques. This research focuses especially on
Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic
(OCA) dataset. Our system was efficient, and it showed a good accuracy of
nearly 85 percent in predicting rating from review
Detailing Sentiment Analysis to Consider Entity Aspects: An Approach for Portuguese Short Texts
Sentiment analysis is useful for identifying trends, or for discovering user preferences, which can later be
applied to campaign targeting or recommendations. In this paper, we describe an approach to classify the
sentiment polarity regarding aspects, and how this technique was used in a previous system, for short
texts in Portuguese, giving it greater sensitivity to detail.
Aspect extraction is done by locating candidates for aspect as expressions having a relationship with the
entity and possibly some polarized term, through rules based on POS tags. For each aspect, the sentiment
polarity is determined by a Maximum Entropy classifier, whose features depend on the entity mention,
on the aspect and its support text, including negation detection, bigrams, POS tags, and sentiment lexiconbased
polarity clues. For aspect sentiment, our classifier evaluation indicated a precision of 68% for the
positive class and 73% for the negative class, with the dataset used in our research.SmartSeg project, which is co-funded through Portugal 2020’s "R&D Incentive System - Individual Projects" program, grant number "POCI-01-0247-FEDER-011192
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