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

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

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

Semantics Embedded Sequential Recommendation for E-Commerce Products (SEMSRec)

In Collaborative Filtering methods, tailored recommendations cannot be obtained when the user-item matrix is sparse (i.e., has low user-item interactions such as item ratings or purchases). Conventional recommendation systems (ChoiRec12, HPCRec18, HSPRec19) utilizing mining techniques such as clustering, frequent and sequential pattern mining along with click and purchase similarity measures for item recommendation cannot perform well when the user-item interactions are less, as the number of items keep increasing rapidly. Additionally, they have not explored the integration of semantic information of products extracted from customers\u27 purchase histories into the item matrix and the pattern mining process. To address this problem, this paper proposes (SEMSRec) which integrates semantic information of E-commerce products extracted from purchase histories into all phases of recommendation process (pre-processing, pattern mining and recommendation). This is achieved by i) learning semantic similarities between items from customers\u27 purchase histories using Prod2vec model, ii) leveraging this information to mine semantically rich sequential purchase patterns and, iii) enriching the item matrix with semantic and sequential product purchase information before applying item based collaborative filtering. Thus, SEMSRec can provide Top-K personalized recommendations based on semantic similarities between items without the need for users\u27 ratings on items. Experimental results on publically available E-commerce data set show that SEMSRec provides more relevant recommendations over other existing methods

Extracting High Profit Sequential Feature Groups of Products Using High Utility Sequential Pattern Mining

Creating a set of product features obtained through mining users’ opinions helps retailers identify the attributes (features or aspects) more accurately and discover the most preferred features of a certain product. High Profit Feature Groups are created by extracting such product feature groups such as ‘{batterylife, camera} of a smartphone,’ which results in higher profit for manufacturers and increased consumer satisfaction. The accuracy of opinion-feature extraction systems can be improved if more complex sequential patterns of customer reviews are included in the user-behavior analysis to obtain relevant feature groups. An existing system referred to in this paper as HPFG19_HU uses High Utility Itemset Mining and Aspect-Based Sentiment Analysis to obtain high profit aspects considering the high utility values, but it does not consider the order of occurrences (sequences) of features formed in customers’ opinion sentences that help distinguish similar users and identify more relevant and related high profit product features. This paper proposes a High Profit Sequential Feature Groups based on the High Utility Sequences (HPSFG_HUS) system, which identifies sequential patterns in features. It combines Opinion Mining with High Utility Sequential Pattern Mining. This approach provides more accurate high feature groups, sales profit, and customer satisfaction, as shown by the retailer’s graphs of extracted High Profit Sequential Feature Groups. Experiments with evaluation results of execution time and evaluation metrics show that this system generates higher revenue than the tested existing systems

Mining Twitter Multi-word Product Opinions with Most Frequent Sequences of Aspect Terms

Given a corpus of microblog texts from a social media platform such as Twitter (e.g., “the new iPhone battery life is good, but camera quality is bad”), mining multi-word aspects (e.g., battery life, camera quality) and opinions (e.g., good, bad) of these products is challenging due to the vast amount of data being generated. Aspect-Based Opinion Mining (ABOM) is thus a combination of automatic aspect extraction and opinion mining that allows an enterprise to analyze the data on relevant features of products in detail, saving time and money. Existing Twitter ABOM systems such as Hate Crime Twitter Sentiment (HCTS) and Microblog Aspect Miner (MAM) generally go through the four-step approach of obtaining microblog posts, identifying frequent nouns (candidate aspects), pruning the candidate aspects, and getting opinion polarity. However, they differ in how well they prune their candidate features. This paper proposes a system called Microblog Aspect Sequence Miner (MASM) as an extension of Microblog Aspect Miner (MAM) by replacing the Apriori algorithm with a modified frequent sequential pattern mining algorithm based on CM-SPAM to also enable mining multi-word aspects more efficiently. The proposed system is able to determine the summary of most common aspects (Aspect Category) and their sentiments for a product. Experimental results with evaluation metrics of execution time, precision, recall, and F1-measure indicate that our approach has higher recall and precision than these existing systems on Sanders Twitter corpus dataset

The HSPRec E-Commerce System Open Source Code Implementation

To promote big data application access, usage and deployment, this paper presents a downloadable open source code implementation for an E-Commerce Recommendation system, HSPRec (Historical Sequential Pattern Recommendation System), in JAVA. The HSPRec system is composed of six different modules for generating purchase/click sequential databases, mining sequential patterns, computing click purchase similarities, generating purchase sequential rules, computing weights for frequent purchase patterns through Weighted Frequent Purchase Pattern Miner, and normalization of the user-item ratings to predict level of interest. The source code of each module and the main runner are discussed under four possible headings of running environment, input data files and format, minimum support format, output data files and format. The overall goal of the HSPRec system is to improve E-commerce Recommendation accuracy by incorporating more complex sequential patterns of user purchase and click stream behavior learned through frequent sequential purchase patterns. HSPRec provides more accurate recommendations than the tested comparative systems

Iron III isomaltose induced hypersensitivity reaction

Iron isomaltose is considered as safe form of iron with no test dose recommended. Here, we are describing the case of a patient who experienced allergic reaction with this formulation of iron. A 35-year-old South Asian woman experienced allergic reaction, she had mild wheeze on examination of chest. She was given intranasal oxygen at 2 L/min. She was given intravenous acetaminophen 1 g for pain relief, 45.4 mg intravenous chlorphenaramine and intravenous 100 mg hydrocortisone. Within half an hour, all her symptoms improved and her hypoxia resolved. Her chest wheezing also disappeared. Iron isomaltose, although relatively safe, can cause allergic reaction. Intravenous iron can cause allergic reaction therefore it should be administered at the facility where trained staff is present so that necessary treatment can be given in case of hypersensitivity reaction