Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders

Product recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has enabled various approaches and directions of recommender systems advancement. Worth to mention is the fact that in past years multiple traditional "offline" retail business are gearing more and more towards employing inferential and even predictive analytics both to stock-related problems such as predictive replenishment but also to enrich customer interaction experience. One of the most important areas of recommender systems research and development is that of Deep Learning based models which employ representational learning to model consumer behavioral patterns. Current state of the art in Deep Learning based recommender systems uses multiple approaches ranging from already classical methods such as the ones based on learning product representation vector, to recurrent analysis of customer transactional time-series and up to generative models based on adversarial training. Each of these methods has multiple advantages and inherent weaknesses such as inability of understanding the actual user-journey, ability to propose only single product recommendation or top-k product recommendations without prediction of actual next-best-offer. In our work we will present a new and innovative architectural approach of applying state-of-the-art hierarchical multi-module encoder-decoder architecture in order to solve several of current state-of-the-art recommender systems issues. Our approach will also produce by-products such as product need-based segmentation and customer behavioral segmentation - all in an end-to-end trainable approach. Finally, we will present a couple methods that solve known retail & distribution pain-points based on the proposed architecture.Comment: 2019 22nd International Conference on Control Systems and Computer Science (CSCS

Deep recommender engine based on efficient product embeddings neural pipeline

Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and development of the "perfect" recommendation system. In our paper, we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid model pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking processes have been done using pharma industry retail real-life transactional Big-Data streams.Comment: 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet

Systems Applications of Social Networks

The aim of this article is to provide an understanding of social networks as a useful addition to the standard tool-box of techniques used by system designers. To this end, we give examples of how data about social links have been collected and used in di erent application contexts. We develop a broad taxonomy-based overview of common properties of social networks, review how they might be used in di erent applications, and point out potential pitfalls where appropriate. We propose a framework, distinguishing between two main types of social network-based user selection-personalised user selection which identi es target users who may be relevant for a given source node, using the social network around the source as a context, and generic user selection or group delimitation, which lters for a set of users who satisfy a set of application requirements based on their social properties. Using this framework, we survey applications of social networks in three typical kinds of application scenarios: recommender systems, content-sharing systems (e.g., P2P or video streaming), and systems which defend against users who abuse the system (e.g., spam or sybil attacks). In each case, we discuss potential directions for future research that involve using social network properties.Comment: Will appear in ACM computing Survey

Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

User-oriented recommender systems in retail

User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history

User-oriented recommender systems in retail

User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history

Hybrid Recommender Systems: A Systematic Literature Review

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.Comment: 38 pages, 9 figures, 14 tables. The final authenticated version is available online at https://content.iospress.com/articles/intelligent-data-analysis/ida16320

Peer Priming? A Large-Scale Field Experiment Studying the Impact of Popular Rankings on Demand in Mobile Retail

Consumers on mobile retail apps face significant search costs due to the small screen size of devices. One of the search aid features to improve the search convenience is to show consumers a small set of frequently used searches conducted by peer consumers on the platform as a prime cue. We refer to this feature as the popular ranking search aid (PRSA). Collaborating with Meituan, a leading services mobile app in China, we implement a large-scale field experiment to explore how PRSA affects consumer search activities and purchases. Our analyses generate three key findings. First, PRSA leads to an increase of 18.6% in page views and a 6.4% increase in purchases. Second, the change in shopping behavior emerges through a change in search behavior with more non-directed searches and fewer directed searches. Third, our mediation analysis supports that search behavior mediates the business outcomes. We offer theoretical and managerial implications

Sequential Recommendation Based on Objective and Subjective Features

Nowadays, sequential recommender systems are widely used in E-commerce fields to capture consumers’ dynamic preferences in short terms. Existing transformer-based recommendation models mainly consider consumer preference for the products and some related features, such as price. However, besides such objective features, some subjective features, such as consumers’ preference for product quality, also affect consumers’ purchase decisions. In this paper, we design a Sequential Recommender system based on Objective and Subjective features (SROS). We construct subjective features by using natural language processing to analyze online consumer reviews. Then we design a feature-level multi-head self-attention to explore the interactions between objective features and subjective features and capture consumers’ dynamic preferences for them among different purchases. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model

Development of a travel recommender system

Nowadays, e-commerce is facing the problem of information overload, where users are exposed to a vast amount of content, making it more difficult for users to effectively take quality decisions. The need for delivering the right items at the right moment to each user has resulted in making recommendation systems one of the hot topics in research and technology trends, where a good recommendation system might give a key advantage to an e-commerce over its competitors. Industry-leading companies such as Youtube or Amazon introduced the concept of large-scale recommendation systems, where the number of candidate items to consider for recommendation is enormous, and efficient techniques must be applied. The most common way to deal with large-scale recommendation systems nowadays is to build a retrieval model that retrieves a subset of relevant items for the user, and a ranking model that scores and ranks the set of retrieved items. Research on recommendation systems is continuously evolving, where new approaches produce state-of-the-art results, which especially happens thanks to the rise of deep learning. In this thesis, we describe classical and current approaches to recommendation systems, from content-based methods without assuming latent factors and collaborative-filtering methods like matrix factorization, to hybrid approaches, deep learning-based methods, and state-of-the-art approaches. Precisely, we focus on the concept of context-aware methods and multitask methods, which aim to optimize more than one task at a time. In this master thesis, we focus on developing a recommendation system for Stayforlong as a proof of concept. Firstly, we analyze their data and see that we can opt for a hybrid context-aware model since we have at our disposal user features, item features and context features. Another thing that characterizes the data set that we work with is the abundance of implicit feedback and the scarcity of explicit feedback. This drives us to experiment with different model architectures and approaches, focusing on developing a hybrid model that performs a retrieval task and another hybrid model that performs a ranking task. In addition, we check in our experiments the benefits of adding context to our models, the benefits of jointly training a model that optimizes multiple tasks, and the benefits of training a model on an abundant data set, like implicit feedback, and applying transfer learning to fine-tune on explicit feedback the learned representations. Results show that, in rich data scenarios, a context-aware multitask hybrid model trained on implicit feedback and fine-tuned with explicit feedback outperforms other approaches such as training separate retrieval and ranking models, disregarding implicit feedback or not including context features in the models. Finally, we propose as future work a data pipeline for the recommendation system to be used in production, taking into account data freshness and model re-training