Complements and substitutes are two typical product relationships that deserve consideration in online product recommendation. One of the key objectives of recommender systems is to promote cross-selling, which heavily relies on recommending the appropriate type of products in specific scenarios. Research on consumer behavior has shown that consumers usually prefer substitutes in the browsing stage whereas complements in the purchasing stage. Thus, it is of great importance to identify the complementary and substitutable relationships between products. In this article, we design a neural network based framework that integrates the textual content and non-textual information of online reviews to mine product relationships. For the textual content, we utilize methods such as LDA topic modeling to represent products in a succinct form called “embedding.” To capture the semantics of complementary and substitutable relationships, we design a modeling process that transfers the product embeddings into semantic features and incorporates additional non-textual factors of product reviews. Extensive experiments are conducted to verify the effectiveness of the proposed product relationship mining model. The advantages and robustness of our model are discussed from various perspectives.

Competitiveness degree analysis is a focal point of business strategy and competitive intelligence, aimed to help managers closely monitor to what extent their rivals are competing with them. This article proposes a novel method, namely BCQ, to measure the competitiveness degree between peers from query logs as an important form of user generated contents, which reflects the “wisdom of crowds” from the search engine users’ perspective. In doing so, a bipartite graph model is developed to capture the competitive relationships through conjoint attributes hidden in query logs, where the notion of competitiveness degree for entity pairs is introduced, and then used to identify the competitive paths mapped in the bipartite graph. Subsequently, extensive experiments are conducted to demonstrate the effectiveness of BCQ to quantify the competitiveness degrees. Experimental results reveal that BCQ can well support competitors ranking, which is helpful for devising competitive strategies and pursuing market performance. In addition, efficiency experiments on synthetic data show a good scalability of BCQ on large scale of query logs.

Recent years have witnessed a rise of social publishing, which is a new type of social networking service. Social publishing has certain new features that call for a new way of managing and providing a large volume of documents. A fine data model is expected to evolve dynamically with the up-to-date knowledge, especially the associations that emerge in the context of social publishing. This paper first presents a conceptual schema of social publishing, which evolves to combine the association knowledge that reflects hidden associations in the data. A major constraint of concern is cardinality constraint. During the process of enriching a schema, the constraints to be specified should conform to the existing ones. A set of inference rules is presented for modeling with cardinality constraints. The rules are proven to be sound and complete, which helps to derive cardinality constraints from existing ones. The derived cardinality constraints are also proven to be consistent. © 2012 Wiley Periodicals, Inc.

Functional dependency (FD) is an important type of semantic knowledge reflecting integrity constraints in databases, and has nowadays attracted an increasing amount of research attention in data mining. Traditionally, FD is defined in the light of precise or complete data, and can hardly tolerate partial truth due to imprecise or incomplete data (such as noises, nulls, etc.) that may often exist in massive databases, or due to a very tiny insignificance of tuple differences in a huge volume of data. Based on the notion of functional dependencies with degrees of satisfaction (FDs)d, this article presents an efficient approach to discovering all satisfied (FDs)d using some important results obtained from exploration of (FDs)d properties such as extended Armstrong-like axioms and their derivatives. In this way, many dependencies can be inferred from previously discovered ones without scanning databases, and those unsatisfied ones could be filtered out inside (rather than after) the mining process. Fuzzy relation matrix operation is used to infer transitive dependencies in the mining algorithm. Finally, the efficiency is demonstrated with data experiments. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1089–1110, 2004.

Traditionally, the association rules discovered from categorical databases are like “Apples⇒Beers”, which means by default we focus on the attribute values which are equal to 1. Usually, we cannot deal with the association rules like “Age: 50–60 ∩ Female ⇒ ¬,Overdraw” whose semantics reflects “the Female users between 50 and 60 typically do not Overdraw ”. Here, we call this type of items (like “¬Overdraw”) as “opposite items”. In fact, however, in many categorical databases, “0” value does make sense. In this paper, we will propose a method to discover the association rules, which are composed of not only original items but also opposite items. Hereafter, Some optimizations are applied on the algorithm.