Content-based Recommender Systems: State of the Art and Trends

Lops, Pasquale; Gemmis, Marco de; Semeraro, Giovanni

doi:10.1007/978-0-387-85820-3_3

Cited by 1,367 publications

(833 citation statements)

References 64 publications

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“…In contrast, the content-based approach makes recommendations based on the item's similarity to previous items liked by the target user, without directly relying on the preferences of other users [1], [2]. The collaborative filtering approach recognizes users whose preferences are similar to those of a particular user and recommends items they have liked whereas the content-based approach recommends items similar to those a particular user has liked in the past [9].…”

Section: Recommender Systemsmentioning

confidence: 99%

“…Additionally, content-based RSs still suffer from the recommendations' limited diversity and overspecialization problems, which limit the items recommended to users only to similar items that were previously rated. Thus, users cannot find something unexpected [3], [7], [9].…”

Section: Recommender Systemsmentioning

confidence: 99%

“…Customer reviews about a single item are collected and offered to the users to simplify the decision-making process. For instance, a customer's feedback at Amazon.com or eBay.com can help users in determining whether an item has been valued by the community [9].…”

Section: Customer-oriented Review Recommendationmentioning

confidence: 99%

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A New Sentiment Case-Based Recommender

Aldayel

Ykhlef

2017

IEICE Trans. Inf. & Syst.

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SUMMARYRecommender systems have attracted attention in both the academic and the business areas. They aim to give users more intelligent methods for navigating and identifying complex information spaces, especially in e-commerce domain. However, these systems still have to overcome certain limitations that reduce their performance, such as overspecialization of recommendations, cold-start, and difficulties when items with unequal probability distribution exist. A novel approach addresses the above issues through a case-based recommendation methodology which is a form of content-based recommendation that is well suited to many product recommendation domains, owing to the clear organization of users' needs and preferences. Unfortunately, the experience-based roots of case-based reasoning are not clearly reflected in case-based recommenders. In other words, the concept that product cases, which are usually fixed feature-based tuples, are experiential is not adopted well in case-based recommenders. To solve this problem as well as the recommenders' rating sparsity issue, one can use product reviews which are generated from users' experience with the product a basis of product information. Our approach adapts the use of sentiment scores along with feature similarity throughout the recommendation unlike traditional case-based recommender systems, which tend to depend entirely on pure similarity-based approaches. This paper models product cases with the products' features and sentiment scores at the feature level and product level. Thus, combining user experience and similarity measures improves the recommender performance and gives users more flexibility to choose whether they prefer products more similar to their query or better qualified products. We present the results using different evaluation methods for different case structures, different numbers of similar cases retrieved and multilevel sentiment-approaches. The recommender performance was highly improved with the use of feature-level sentiment approach, which recommends product cases that are similar to the query but favored for customers. key words: recommenders, case-based reasoning (CBR), case-based recommenders, opinion mining

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Section: Recommender Systemsmentioning

confidence: 99%

Section: Recommender Systemsmentioning

confidence: 99%

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A New Sentiment Case-Based Recommender

Aldayel

Ykhlef

2017

IEICE Trans. Inf. & Syst.

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“…Hence, the basic building block of our group recommender is an individual recommender that computes the estimated preference of users for a given item. Individual recommendations in HappyMovie follow a content based approach [61]. This approach, schematized in Figure 1, uses the descriptions of the products to be recommended (obtained with the Web Crawler module), compares them with the descriptions of products rated by the user (obtained with the Preferences Elicitation module), and predicts the rating for the aimed products (computed in the Content Based Estimation module) by computing the average of the most similar rated products.…”

Section: Individual Recommendation Modulesmentioning

confidence: 99%

“…We have chosen a content-based approach to estimate the rating users would assign to a new movie [61]. An alternative approach is a collaborative filtering approach [62].…”

Section: Content Based Estimationmentioning

confidence: 99%

Development of a group recommender application in a Social Network

Quijano-Sánchez

Díaz-Agudo

Recio-García

2014

Knowledge-Based Systems

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In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.

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e‐Commerce Recommender Systems

Boehmer

Jung

Wash

2015

The International Encyclopedia of Digital Communication and Society

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Recommender systems are a vital part of the way today's information society deals with information overload. Recommender systems help e‐commerce retailers to choose items to display based on customers' preferences, help users personalize search, and help streaming services create customized playlists. This entry describes multiple kinds of recommender systems and how they work. It also explains their historical and intellectual context, shows how they might affect users, and discusses current challenges.

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Content-based Recommender Systems: State of the Art and Trends

Cited by 1,367 publications

References 64 publications

A New Sentiment Case-Based Recommender

A New Sentiment Case-Based Recommender

Development of a group recommender application in a Social Network

e‐Commerce Recommender Systems

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