The most widely used recommendation techniques tend to recommend similar items to those liked by a user in the past or items that similar users liked based on the hypothesis that users have a stable behavior over time and that similar users share similar tastes. However, these methodologies show limited performance when new users or less popular items appear-often called a cold start-while others can lead to overspecialization of the recommended items. In addition, because they tend to ignore the underlying structure of user preferences and don't evaluate the hidden concepts under which items are selected, their accuracy decreases when trading items with unequal probability distributions. Previous research, mainly in the fields of market research and customer behavior analysis, has revealed the existence of patterns that determine the structure of users' market baskets, which is defined as the set of items bought by one customer in a single visit to a store. Market basket analysis (MBA) refers to the search for meaningful associations in customer purchase data, and it aims to discover the patterns that define the composition of those baskets. 2 The market basket domain is characterized by the existence of a large number of items and transactions that consist of co-occurring items. More than simply predicting whether a single item will be liked by a user, the intention is to capture the presence or absence of an item within a concrete buying concept and to become able to recommend complementary items. Most of the current recommendation methodologies don't take into account these cooccurrences, and the association rules (ARs) methodology that has been used as a basis for recommendations in such cases has several limitations-such as computational and evaluation difficulties-when applied to large datasets. 3 There's a need for intelligent recommendation methodologies that can generate valuable recommendations based on user R ecommender systems (RSs) have been used in numerous domains to support both users in handling information overload and finding adequate items to cover their needs and providers in identifying user needs and increasing the amount and diversity of the items they sell. 1 R e c o m m e n d e R S y S t e m S JaNuarY/FEbruarY 2015 www.computer.org/intelligent 21 habits and purchase patterns, yet that overcome the drawbacks of the current recommendation techniques. Here, we present an analysis and recommendation approach for situations in which a user's experience depends on the set of items selected together more than on each item's stand-alone attributes. Case-based reasoning (CBR) is particularly appropriate because the sets of items selected together can be adequately modeled and compared. In addition, the performance of a casebased reasoner benefits from the existence of a large amount of data, such as in the MBA domain. To determine the structure of user preferences and generate valuable recommendations, the implemented methodology uses a hierarchical categorization of items in transactions. By evaluating sets of selected items, the system can identify those items selected within concrete concepts and recommend them in similar situations to new or existing users. RSs are software tools and techniques for information retrieval and filtering that aim to provide meaningful and effective item recommendations to the active user. 4 The term item refers to the type of entity (product, service, information, and so on) being recommended; it depends on the application area and on the specific system's objectives. RSs usually generate a set of (top-N) items expected to be liked by the user or intend to predict whether a specific item will be of interest. The widely used recommendation methodologies in commercial applications can be mainly divided into collaborative filtering (CF) and content-based (CB). Figure 1 illustrates their main differences. In CF recommendation techniques, items among those liked by similar users ("neighbors") are recommended to the active user. A user profile is built of the items that the user has rated highly, thus similarities in user tastes are deduced from previous ratings. Although widely used in commercial applications, collaborative RSs still have to overcome scalability and cold-start problems that limit their performance. 5 On the other hand, in CB techniques, user profiles are built from the characteristics of the items that a user has rated highly, and the items that he or she hasn't yet tried are compared against them. The items with the higher estimated possibility of being liked are then recommended. 4,6 Because CB techniques rely on more specific information about users and items, they're able to recommend new items. However, they must overcome the recommendations' limited diversity and possible overspecialization. Various hybrid approaches have been proposed to leverage the strengths of both techniques, overcome their current limitations, and improve their recommendation accuracy. 1,6 The following are the basic recommendation techniques identified in the literature 1 : • CF (memory-based, model-based), • CB filtering (neural networks, probabilistic models, naïve Bayes classifier),
Another research REF proposed a case-based recommendation approach for market basket analysis using a hierarchical model.
A Case-Based Recommendation Approach for Market Basket Data
IEEE Intelligent Systems
IEEE Intelligent Systems
Computer Science