An Efficient Cross-Domain Recommendation Technique in Cold-Start Situations

Most of the recent studies on recommender systems are focused on single domain recommendation systems. In the single domain recommendation systems, the items that are used for training and test data set are belongs to within the same domain. Cross-site domains or item recommendations in multi-domain environment are available in Amazon i.e. it incorporate two or more domains. Few research studies are done on the cross-site recommendation systems. Cross-site recommendations provide the relationship between the two sets of items from various domains. They can provide the extra information about the users of a target domain and recommendations will be done based on that. In this paper, we will study cross-site recommendation model on the cold start situation, where the purchase history is not available for the new user. Cold-start is the well-known issue in the area of recommendation systems. It seriously affect the recommendations in the collaborative filtering approaches. In this paper, we propose a new solution to recommend products from e-commerce websites to users at social networking sites. a noteworthy issue is how to leverage knowledge from social networking websites when there is no purchase history for a customer especially in cold start situations.in particular we proposed the solution for cold start recommendation by linking the users across social networking sites and e-commerce websites i.e. customers who have social network identities and have purchased on e-commerce websites as a bridge to map user’s social networking features in to another feature representation which can be easier for product recommendation. Here we propose to learn by using recurrent neural networks both user’s and product’s feature representations called user embedding and product embedding from the data collected from e-commerce website and then apply a modified gradient boosting trees method to transform user’s social networking features in to user embedding. Once found, then develop a feature-based matrix factorization approach which can leverage the learnt user embedding for the cold-start product recommendation. Experimental results shows that our approach effectively works and gives the best recommended results in cold start situations

Three Essays on Consumers\u27 Activities in the Online Domain

Nowadays, with the explosive growth in the usage of the Internet, consumers are performing all kinds of activities over the Internet like searching or buying. We want to study the different activities of consumers in the online domain. In our daily lives, people are often making various kinds of product purchases. When making such purchases, a lot of factors can affect consumers\u27 decisions. This includes the nature of the product category, and especially in the online domain, the nature of their search activities. In the first essay/chapter, we develop an econometric model to understand the relationships between different dimensions of on-line search and purchase behavior. Our approach uses endogeneity corrections to develop a model that is more correct than the typical non-endogeneity corrected model. Thus we believe our results to be truly reflective of what is happening in the search-buying domain. We use extensive empirical data to test several hypotheses that we developed. Parameters from our model estimations reveal that there are interesting variations in the search-purchase behavior relationships across types of product categories. This difference is especially evident between utilitarian and hedonic goods. Our findings have important theoretical and managerial implications. The amount of information in text reviews is tremendously greater than that in typical numerical data. A major challenge for marketers is how to extract the most relevant information from this big data source. In our second essay/chapter, we do this by using a text mining methodology that draws on machine learning algorithms. We collect data using a Java WebCrawler type programming approach. We use a word-based model to predict consumers\u27 recommendations. Model prediction accuracy was high. In the marketing literature there has been almost no work where such a methodology has been used to make predictions of recommendations based on big data stemming from textual information. An interesting finding from our research is that as the number of textual features increases, the predictive accuracy of the model increases only up to a point. Beyond that, inclusion of more words in the model leads to a decrease in predictive accuracy. We also use a diagnostic approach to identify key words that are determinants of user recommendations. Since our model deals with big data, we address in details the issue of scalability; our computations show that our approach is very scalable. Potential for marketing implications seems considerable. Marketers are always interested in predicting market sales so that they can arrange the firm activities accordingly. In the meantime, this market sales information can also help the consumers to make right buying decisions. However the high cost and long period of collecting the available data with a lag makes it very inconvenient and out of date. With the rise of multi-social media sharing websites such as YouTube, Flickr, and various blogs, consumers can search and learn various types of information from these websites. The availability of large amounts of data on the Internet enables us to use large scale data mining algorithms for solving complex problems. The users\u27 online searching activities can be captured for predicting the market sales. In the third essay/chapter, we focus on the impacts of different search behavior and marketing outcomes like product sales. We examined the three major online search areas including text, image, and video from search engines like Google to help us accurately and easily predict the sales of automobiles. We believe that our work here opens a brand new arena for using multimedia search activities and will have a big impact on marketing sciences

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

Social Network Platform For Business Growth Using Ecommerce Product Recommendation

Present day's online shopping has accomplished an enormous disrepute privileged less measure of time. As of late few ecommerce sites has been created their functionalities to a point with the end goal that they suggest the product for their clients alluding to the availability of the clients to the social media and give coordinate login from such social media, (for example, facebook, Google+ ,and so forth). For suggesting the clients that are absolutely new to the sites, we utilize novel answer for cross-webpage cold-start product recommendation that goes for prescribing products from online business sites. In particular, we propose learning the two clients and products include portrayals from information gathered from internet business sites utilizing repetitive Matrix Factorization to change client's social networking highlights into client embeddings. We at that point build up a feature-based matrix factorization approach which can control the learnt client embedding for cold-start product recommendation

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

Positive Example Learning for Content-Based Recommendations: A Cost-Sensitive Learning-Based Approach

Existing supervised learning techniques can support product recommendations but are ineffective in scenarios characterized by single-class learning; i.e., training samples consisted of some positive examples and a much greater number of unlabeled examples. To address the limitations inherent in existing single-class learning techniques, we develop COst-sensitive Learning-based Positive Example Learning (COLPEL), which constructs an automated classifier from a singleclass training sample. Our method employs cost-proportionate rejection sampling to derive, from unlabeled examples, a subset likely to feature negative examples, according to the respective misclassification costs. COLPEL follows a committee machine strategy, thereby constructing a set of automated classifiers used together to reduce probable biases common to a single classifier. We use customers’ book ratings from the Amazon.com Web site to evaluate COLPEL, with PNB and PEBL as benchmarks. Our results show that COLPEL outperforms both PNB and PEBL, as measured by its accuracy, positive F1 score, and negative F1 score

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI