Case-Based Recommendation

Smyth, Barry

doi:10.1007/978-3-540-72079-9_11

Cited by 155 publications

(115 citation statements)

References 82 publications

(104 reference statements)

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“…The collaborative filtering approach recommends items that do not exist in the active user's profile but those that other users have rated highly. 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%

“…Furthermore, there are noticeable matches between the retrieval in CBR and the handling of the user query in RSs; the user query works as a new problem specification, the item descriptions are cases in the case base, and the recommended items are retrieved based on their similarity to the user's request. Hence, case-based RSs can be distinguished from other types of content-based systems using two mechanisms: (1) item representation as a case, i.e., case model, and (2) recommendations based on retrieving cases similar to a user's query, i.e., similarity assessment [1], [2], [10].…”

Section: Case-based Recommendersmentioning

confidence: 99%

“…If a user's query is over-specified (i.e., no product precisely matches the query), similarity-based retrieval techniques can retrieve a set of useful similar products. In the same way, if the user's query is underspecified (i.e., too many items exactly match the query), similarity techniques permit ranking products and even eliminating those with the lowest similarity marks [1], [2].…”

Section: Similarity Assessmentmentioning

confidence: 99%

“…Symmetric similarity metric is used if there is no bias in favor of either the upper or the lower value of a numeric feature; otherwise, asymmetric will be used. Moreover, evaluating the similarity of nominal features necessitates extra domain knowledge [1], [7]. Examples of local feature-based similarity measures are presented in Table 2 where Max i and Min i refer to the maximum and the minimum values of the i-th feature.…”

Section: S Imilarity(t C)mentioning

confidence: 99%

“…Most of these techniques show inadequate performance when new users or infrequent items appear (coldstart problem) whereas others may tend to overspecialize the resulting items. Furthermore, as they tend to ignore the fundamental structure of users' first choice, and do not evaluate the hidden concepts under which items are selected, their accuracy drops when the item has unequal probability distributions [1]- [3]. A better approach, which addresses the above problems, is through a case-based recommendation methodology.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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%