Factorizing Historical User Actions for Next-Day Purchase Prediction

2022 ◽  
Vol 16 (1) ◽  
pp. 1-26
Author(s):  
Bang Liu ◽  
Hanlin Zhang ◽  
Linglong Kong ◽  
Di Niu
Keyword(s):  
User Behavior ◽  
Purchase Behavior ◽  
Time Decay ◽  
Music Recommendation ◽  
Unified Framework ◽  
Transaction Data ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Real World Datasets ◽  
User Actions

It is common practice for many large e-commerce operators to analyze daily logged transaction data to predict customer purchase behavior, which may potentially lead to more effective recommendations and increased sales. Traditional recommendation techniques based on collaborative filtering, although having gained success in video and music recommendation, are not sufficient to fully leverage the diverse information contained in the implicit user behavior on e-commerce platforms. In this article, we analyze user action records in the Alibaba Mobile Recommendation dataset from the Alibaba Tianchi Data Lab, as well as the Retailrocket recommender system dataset from the Retail Rocket website. To estimate the probability that a user will purchase a certain item tomorrow, we propose a new model called Time-decayed Multifaceted Factorizing Personalized Markov Chains (Time-decayed Multifaceted-FPMC), taking into account multiple types of user historical actions not only limited to past purchases but also including various behaviors such as clicks, collects and add-to-carts. Our model also considers the time-decay effect of the influence of past actions. To learn the parameters in the proposed model, we further propose a unified framework named Bayesian Sparse Factorization Machines. It generalizes the theory of traditional Factorization Machines to a more flexible learning structure and trains the Time-decayed Multifaceted-FPMC with the Markov Chain Monte Carlo method. Extensive evaluations based on multiple real-world datasets demonstrate that our proposed approaches significantly outperform various existing purchase recommendation algorithms.

scholarly journals Music Recommendation Algorithms: Discovering Weekly or Discovering Weakly?

2019 ◽  
Author(s):  
Jennie Silber
Keyword(s):  
User Experience ◽  
Music Recommendation ◽  
Media Communication ◽  
Recommendation Algorithms ◽  
Potential Benefits ◽  
Cultural Reality ◽  
Winner Take All ◽  
User Actions ◽  
Music Streaming ◽  
The Way

This thesis analyzes and assesses the cultural impact and economic viability that the top music streaming platforms have on the consumption and discovery of music, with a specific focus on recommendation algorithms. Through the support of scholarly and journalistic research as well as my own user experience, I evaluate the known constructs that fuel algorithmic recommendations, but also make educated inferences about the variables concealed from public knowledge. One of the most significant variables delineated throughout this thesis is the power held by human curators and the way they interact with algorithms to frame and legitimize content. Additionally, I execute my own experiment by creating new user profiles on the two streaming platforms popularly used for the purpose of discovery, Spotify and SoundCloud, and record each step of the music discovery process experienced by a new user. After listening to an equal representation of all genre categories within each platform, I then compare the genre, release year, artist status, and content promotion gathered from my listening history to the algorithmically-generated songs listed in my ‘Discover Weekly’ and ‘SoundCloud Weekly’ personalized playlists. The results from this experiment demonstrate that the recommendation algorithms that power these discovery playlists intrinsically facilitate the perpetuation of a stardriven, “winner-take-all” marketplace, where new, popular, trendy, music is favored, despite how diverse of a selection the music being listened to is.The content of this thesis is significant to understanding the culture of music streaming and is also contributory to the field of media communication. Unlike any other scholarly research, the “walk-through” experiment uniquely tracks a new user experience through the cognizant application of user actions and directly assesses the factors that challenge successful music recommendation. This method of research specifically highlights the influence that music streaming platforms have not only as tastemakers, but more importantly, as gatekeepers of cultural information, shaping the perceived value and relevance of artists and genres through recommendation. This thesis underlines the challenges faced by recommendation systems in providing the novel, yet relevant recommendations necessary to satisfy the needs of users, while also providing wide-ranging, yet representative recommendations to stimulate diversity and creativity within society.These challenges include the subjective organization of songs and genres within a platform’s interface, the misrepresentation of songs and artists within genre-based playlists, the use of user actions (skips, likes, dislikes, passive listening, drifting, etc.) as an assertion of one’s likes and dislikes, as well as the manipulation of hit-producing market trends. This thesis delves deeply into each challenge and the ways they affect the inaccuracy, subjectivity, and homogeneity currently projected through music streaming recommendations. Lastly, this thesis addresses the potential benefits and apprehensions of future contextually aware technology and its ability to reshape the way recommendation algorithms gather and process user listening data. Ultimately, my hope is that this research sheds light on the responsibility of music listeners, but more importantly, of music distributors and curators, as taste makers and gatekeepers, to act progressively and ethically in constructing the cultural reality we live in.

scholarly journals Biogeography optimization algorithm based next web page prediction using weblog and web content features

2020 ◽  
Vol 9 (2) ◽  
pp. 327
Author(s):  
Roshan Anant Gangurde ◽  
Binod Kumar
Keyword(s):  
Optimization Algorithm ◽  
User Behavior ◽  
Web Content ◽  
Web Page ◽  
Search Queries ◽  
Proposed Model ◽  
Fitness Value ◽  
Immigration And Emigration ◽  
User Actions ◽  
Better Than

<span lang="EN-US">Recommendation of web page as per users’ interest is a broad and important area of research. Researcher adopts user behavior from actions present in cookies, logs and search queries. This paper has utilized a prior webpage fetching model using web page prediction. For this purpose, web content in form of text and weblog features are analyzed. As per dynamic user behavior, proposed model LWPP-BOA (Logistic Web Page Prediction By Biogeography Optimization Algorithm) predict page by using genetic algorithm. Based on user actions, weblog feature are developed in form of association rules, while web content gives a set of relevant text patterns. Page prediction as per random user behavior is enhanced by means of Biogeography Optimization Algorithm where crossover operation is performed as per immigration and emigration values. Here population updation depends on other parameters of chromosome except fitness value. Experiments are conducted on real dataset having web content and weblogs. Results are compared using precision, coverage, M-Metric, MAE and RMSE parameters and it indicates that the proposed work is better than other approaches already in use.</span>

scholarly journals Understanding Users' Budgets for Recommendation with Hierarchical Poisson Factorization

2017 ◽  
Author(s):  
Yunhui Guo ◽  
Congfu Xu ◽  
Hanzhang Song ◽  
Xin Wang
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Online Shopping ◽  
State Of The Art ◽  
Generative Model ◽  
Personal Consumption ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Real World Datasets ◽  
Consumption Habits

People consume and rate products in online shopping websites. The historical purchases of customers reflect their personal consumption habits and indicate their future shopping behaviors. Traditional preference-based recommender systems try to provide recommendations by analyzing users' feedback such as ratings and clicks. But unfortunately, most of the existing recommendation algorithms ignore the budget of the users. So they cannot avoid recommending users with products that will exceed their budgets. And they also cannot understand how the users will assign their budgets to different products. In this paper, we develop a generative model named collaborative budget-aware Poisson factorization (CBPF) to connect users' ratings and budgets. The CBPF model is intuitive and highly interpretable. We compare the proposed model with several state-of-the-art budget-unaware recommendation methods on several real-world datasets. The results show the advantage of uncovering users' budgets for recommendation.

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Shengsheng Qian ◽  
Jun Hu ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Visual Information ◽  
Representation Learning ◽  
Fake News ◽  
Unified Framework ◽  
Model Learning ◽  
Convolutional Network ◽  
Textual Information ◽  
Convolutional Networks ◽  
Real World Datasets

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

scholarly journals Multi-agent Attentional Activity Recognition

2019 ◽  
Author(s):  
Kaixuan Chen ◽  
Lina Yao ◽  
Dalin Zhang ◽  
Bin Guo ◽  
Zhiwen Yu
Keyword(s):  
Activity Recognition ◽  
State Of The Art ◽  
Body Part ◽  
Body Parts ◽  
Temporal Attention ◽  
Attention Model ◽  
Proposed Model ◽  
Collective Motions ◽  
Multi Agent ◽  
Real World Datasets

Multi-modality is an important feature of sensor based activity recognition. In this work, we consider two inherent characteristics of human activities, the spatially-temporally varying salience of features and the relations between activities and corresponding body part motions. Based on these, we propose a multi-agent spatial-temporal attention model. The spatial-temporal attention mechanism helps intelligently select informative modalities and their active periods. And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions. With a joint recognition goal, the agents share gained information and coordinate their selection policies to learn the optimal recognition model. The experimental results on four real-world datasets demonstrate that the proposed model outperforms the state-of-the-art methods.

scholarly journals Neural network model for multimodal recognition of human aggression

2020 ◽  
pp. 132-149
Author(s):  
М.Ю. Уздяев
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Space ◽  
Smart Environments ◽  
High Fidelity ◽  
Cyberphysical Systems ◽  
Human Aggression ◽  
Proposed Model ◽  
User Actions

Увеличение количества пользователей социокиберфизических систем, умных пространств, систем интернета вещей актуализирует проблему выявления деструктивных действий пользователей, таких как агрессия. При этом, деструктивные действия пользователей могут быть представлены в различных модальностях: двигательная активность тела, сопутствующее выражение лица, невербальное речевое поведение, вербальное речевое поведение. В статье рассматривается нейросетевая модель многомодального распознавания человеческой агрессии, основанная на построении промежуточного признакового пространства, инвариантного виду обрабатываемой модальности. Предлагаемая модель позволяет распознавать с высокой точностью агрессию в условиях отсутствия или недостатка информации какой-либо модальности. Экспериментальное исследование показало 81:8% верных распознаваний на наборе данных IEMOCAP. Также приводятся результаты экспериментов распознавания агрессии на наборе данных IEMOCAP для 15 различных сочетаний обозначенных выше модальностей. Growing user base of socio-cyberphysical systems, smart environments, IoT (Internet of Things) systems actualizes the problem of revealing of destructive user actions, such as various acts of aggression. Thereby destructive user actions can be represented in different modalities: locomotion, facial expression, associated with it, non-verbal speech behavior, verbal speech behavior. This paper considers a neural network model of multi-modal recognition of human aggression, based on the establishment of an intermediate feature space, invariant to the actual modality, being processed. The proposed model ensures high-fidelity aggression recognition in the cases when data on certain modality are scarce or lacking. Experimental research showed 81.8% correct recognition instances on the IEMOCAP dataset. Also, experimental results are given concerning aggression recognition on the IEMOCAP dataset for 15 different combinations of the modalities, outlined above.

scholarly journals BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks

2021 ◽  
Vol 27 (7) ◽  
pp. 667-692
Author(s):  
Lamia Berkani ◽  
Lylia Betit ◽  
Louiza Belarif
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Recommendation Algorithms ◽  
Clustering Approach ◽  
Real World Datasets ◽  
Multiview Clustering ◽  
Improving Accuracy

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

scholarly journals Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification

2019 ◽  
Vol 33 ◽  
pp. 3304-3311
Author(s):  
Chen Chen ◽  
Haobo Wang ◽  
Weiwei Liu ◽  
Xingyuan Zhao ◽  
Tianlei Hu ◽  
...  
Keyword(s):  
Learning Model ◽  
Two Stage ◽  
Second Stage ◽  
Proposed Model ◽  
Latent Space ◽  
Label Correlations ◽  
Factorization Machine ◽  
Classification Tasks ◽  
Real World Datasets ◽  
Embedding Methods

Label embedding has been widely used as a method to exploit label dependency with dimension reduction in multilabel classification tasks. However, existing embedding methods intend to extract label correlations directly, and thus they might be easily trapped by complex label hierarchies. To tackle this issue, we propose a novel Two-Stage Label Embedding (TSLE) paradigm that involves Neural Factorization Machine (NFM) to jointly project features and labels into a latent space. In encoding phase, we introduce a Twin Encoding Network (TEN) that digs out pairwise feature and label interactions in the first stage and then efficiently learn higherorder correlations with deep neural networks (DNNs) in the second stage. After the codewords are obtained, a set of hidden layers is applied to recover the output labels in decoding phase. Moreover, we develop a novel learning model by leveraging a max margin encoding loss and a label-correlation aware decoding loss, and we adopt the mini-batch Adam to optimize our learning model. Lastly, we also provide a kernel insight to better understand our proposed TSLE. Extensive experiments on various real-world datasets demonstrate that our proposed model significantly outperforms other state-ofthe-art approaches.

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

scholarly journals Be Relevant, Non-Redundant, and Timely: Deep Reinforcement Learning for Real-Time Event Summarization

2020 ◽  
Vol 34 (05) ◽  
pp. 9410-9417
Author(s):  
Min Yang ◽  
Chengming Li ◽  
Fei Sun ◽  
Zhou Zhao ◽  
Ying Shen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Language Processing ◽  
Semantic Features ◽  
Unified Framework ◽  
Time Step ◽  
Dynamic Memory ◽  
The Arts ◽  
Real World Datasets ◽  
Event Summarization

Real-time event summarization is an essential task in natural language processing and information retrieval areas. Despite the progress of previous work, generating relevant, non-redundant, and timely event summaries remains challenging in practice. In this paper, we propose a Deep Reinforcement learning framework for real-time Event Summarization (DRES), which shows promising performance for resolving all three challenges (i.e., relevance, non-redundancy, timeliness) in a unified framework. Specifically, we (i) devise a hierarchical cross-attention network with intra- and inter-document attentions to integrate important semantic features within and between the query and input document for better text matching. In addition, relevance prediction is leveraged as an auxiliary task to strengthen the document modeling and help to extract relevant documents; (ii) propose a multi-topic dynamic memory network to capture the sequential patterns of different topics belonging to the event of interest and temporally memorize the input facts from the evolving document stream, avoiding extracting redundant information at each time step; (iii) consider both historical dependencies and future uncertainty of the document stream for generating relevant and timely summaries by exploiting the reinforcement learning technique. Experimental results on two real-world datasets have demonstrated the advantages of DRES model with significant improvement in generating relevant, non-redundant, and timely event summaries against the state-of-the-arts.

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