Semantic enhanced Markov model for sequential E-commerce product recommendation

To model sequential relationships between items, Markov Models build a transition probability matrix P of size n× n, where n represents number of states (items) and each matrix entry p(i,j) represents transition probabilities from state i to state j. Existing systems such as factorized personalized Markov chains (FPMC) and fossil either combine sequential information with user preference information or add the high-order Markov chains concept. However, they suffer from (i) model complexity: an increase in Markov Model’s order (number of states) and separation of sequential pattern and user preference matrices, (ii) sparse transition probability matrix: few product purchases from thousands of available products, (iii) ambiguous prediction: multiple states (items) having same transition probability from current state and (iv) lack of semantic knowledge: transition to next state (item) depends on probabilities of items’ purchase frequency. To alleviate sparsity and ambiguous prediction problems, this paper proposes semantic-enabled Markov model recommendation (SEMMRec) system which inputs customers’ purchase history and products’ metadata (e.g., title, description and brand) and extract products’ sequential and semantic knowledge according to their (i) usage (e.g., products co-purchased or co-reviewed) and (ii) textual features by finding similarity between products based on their characteristics using distributional hypothesis methods (Doc2vec and TF-IDF) which consider the context of items’ usage. Next, this extracted knowledge is integrated into the transition probability matrix P to generate personalized sequential and semantically rich next item recommendations. Experimental results on various E-commerce data sets exhibit an improved performance by the proposed model

A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

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

Mining Integrated Sequential Patterns From Multiple Databases

Existing work on multiple databases (MDBs) sequential pattern mining cannot mine frequent sequences to answer exact and historical queries from MDBs having different table structures. This article proposes the transaction id frequent sequence pattern (TidFSeq) algorithm to handle the difficult problem of mining frequent sequences from diverse MDBs. The TidFSeq algorithm transforms candidate 1-sequences to get transaction subsequences where candidate 1-sequences occurred as (1-sequence, itssubsequenceidlist) tuple or (1-sequence, position id list). Subsequent frequent i-sequences are computed using the counts of the sequence ids in each candidate i-sequence position id list tuples. An extended version of the general sequential pattern (GSP)-like candidate generates and a frequency count approach is used for computing supports of itemset (I-step) and separate (S-step) sequences without repeated database scans but with transaction ids. Generated patterns answer complex queries from MDBs. The TidFSeq algorithm has a faster processing time than existing algorithms

A Uniform Approach for Selecting Views and Indexes in a Data Warehouse

Careful selection of aggregate views and some of their most used indexes to materialize in a data warehouse reduces the warehouse query response time as well as warehouse maintenance cost under some storage space constraint. Data Warehouses collect and store large amounts of integrated enterprise data from a number of independent data sources over a long period of time. Warehouse data are used for online analytical processing to assist management in making quick and competitive business decisions. Precomputing and storing summary tables (materialized views) reduces the amount of time needed to recompute these views across several source tables in order to answer complex warehouse queries. A data cube is an elegant way for representing aggregate information in a Warehouse and is an n-dimensional view with 2 n subviews. This paper presents a uniform technique for selecting the subviews of the data cube and their indexes to materialize in order to produce the best resultant benefit to t..

Selecting and Materializing Horizontally Partitioned Warehouse Views

Data warehouse views typically store large aggregate tables based on a subset of dimension attributes of the main data warehouse fact table. Aggregate views can be stored as 2 n subviews of a data cube with n attributes. Methods have been proposed for selecting only some of the data cube views to materialize in order to speed up query response time, accommodate storage space constraint and reduce warehouse maintenance cost. This paper proposes a method for selecting and materializing views, which selects and horizontally fragments a view, recomputes the size of the stored partitioned view while deciding further views to select. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Data warehouse; Views; Fragmentation; Performance benet 1. Introduction Decision support systems (DSS) used by business executives require analyzing snapshots of departmental databases over several periods of time. Departmental databases of the same organization (e.g., a bank) may be stored on dier..

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