Improving e-commerce product recommendation using semantic context and sequential historical purchases

Collaborative Filtering (CF)-based recommendation methods suffer from (i) sparsity (have low user–item interactions) and (ii) cold start (an item cannot be recommended if no ratings exist). Systems using clustering and pattern mining (frequent and sequential) with similarity measures between clicks and purchases for next-item recommendation cannot perform well when the matrix is sparse, due to rapid increase in number of items. Additionally, they suffer from: (i) lack of personalization: patterns are not targeted for a specific customer and (ii) lack of semantics among recommended items: they can only recommend items that exist as a result of a matching rule generated from frequent sequential purchase pattern(s). To better understand users’ preferences and to infer the inherent meaning of items, this paper proposes a method to explore semantic associations between items obtained by utilizing item (products’) metadata such as title, description and brand based on their semantic context (co-purchased and co-reviewed products). The semantics of these interactions will be obtained through distributional hypothesis, which learns an item’s representation by analyzing the context (neighborhood) in which it is used. The idea is that items co-occurring in a context are likely to be semantically similar to each other (e.g., items in a user purchase sequence). The semantics are then integrated into different phases of recommendation process such as (i) preprocessing, to learn associations between items, (ii) candidate generation, while mining sequential patterns and in collaborative filtering to select top-N neighbors and (iii) output (recommendation). Experiments performed on publically available E-commerce data set show that the proposed model performed well and reflected user preferences by recommending semantically similar and sequential products

Maximizing Bigdata Retrieval: Block as a Value for NoSQL over SQL

This paper presents NoSQL Over SQL Block as a Value Database (NOSD), a system that speeds up data retrieval time and availability in very large relational databases. NOSD proposes a Block as a Value model (BaaV). Unlike a relational database model where a relation is R(K, A1,A2, An), with a key attribute K and a set of attributes of the relation: A1, A2, An, BaaV represents a relation R(K, r1, r2, rn) with a key attribute K and a set of n relations called blocks. Each r contains a set of its own attributes denoted as r(k, a1,a2, an) with a key attribute k and a set of n attributes. The relations r1, r2, rn in R are related through foreign key relationships to a super relation R with primary key K. The BaaV model is then denoted in a keyed block format R K, B, where K is a key to a block of values B of partial relations implemented on NoSQL databases and replicating existing large relational database systems. As opposed to conventional systems such as Zidian, Google\u27s Spanner, SparkSQL and Simple Buttom-Up (SBU) which implement SQL over NoSQL and replicate data into different nodes, NOSD implements NoSQL over SQL and uses Lucene functionality on NoSQL to enhance data retrieval costs. Experimenting with our proposed model, we demonstrated the performance of NOSD under the following conditions to prove its novelty (a) scan free queries, and (b) bounded queries on NoSQL databases. We showed that NOSD (a) performs excellently than ordinary relational databases (b) guarantees no scans for no scan queries (c) allows parallelization in query execution, and (d) can be deployed into existing SQL databases with guaranteed horizontal scalability, data retention and accurate autonomous data replication. Using existing benchmark systems, we demonstrated that NOSD outperforms existing SQL databases, SQL over NoSQL systems and is novel in ensuring that existing large SQL database systems utilize the functionalities of NoSQL databases without data loss. A1, A2, A