A Novel Cross-Site Product Recommendation Method in Cold Start Circumstances

In the last 20 years, more than 250 research articles were published about research paper recommender systems. In the recent years, the farthest point between internet business applications such as e-commerce websites and social networking applications has interpersonal communication and it has turned out to be progressively obscured. Numerous e-commerce web and mobile applications allowing social logging mechanism where their clients can signing in their websites using their personal social network identities such as twitter or Facebook accounts etc. users can likewise post their recently purchased items on social networking websites with the appropriate links to the e-commerce product web pages. 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 to 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 into another feature representation which can be easier for a 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 into user embedding. Once found, then develop a feature-based matrix factorization approach which can leverage the learned user embedding for the cold-start product recommendation. Experimental results show that our approach effectively works and gives the best-recommended results in cold start situations

USING FILTERS IN TIME-BASED MOVIE RECOMMENDER SYSTEMS

On a very high level, a movie recommendation system is one which uses data about the user, data about the movie and the ratings given by a user in order to generate predictions for the movies that the user will like. This prediction is further presented to the user as a recommendation. For example, Netflix uses a recommendation system to predict movies and generate favorable recommendations for users based on their profiles and the profiles of users similar to them. In user-based collaborative filtering algorithm, the movies rated highly by the similar users of a particular user are considered as recommendations to that user. But users’ preferences vary with time, which often affects the efficacy of the recommendation, especially in a movie recommendation system. Because of the constant variation of the preferences, there has been research on using time of rating or watching the movie as a significant factor for recommendation. If time is considered as an attribute in the training phase of building a recommendation model, the model might get complex. Most of the research till now does this in the training phase, however, we study the effect of using time as a factor in the post training phase and study it further by applying a genre-based filtering mechanism on the system. Employing this in the post training phase reduces the complexity of the method and also reduces the number of irrelevant recommendations

When Does the Influencer Matter?

The purpose of this research is to identify what factors contribute to the effectiveness of social media influencers’ posts. The first phase of this project studied people’s initial feelings towards social media influencers using a focus group. The results indicated that social media influencers are in fact effective and influential. The second phase of this study tested what factors increase and decrease the effectiveness of a social media influencers post, and what factors will get them the most engagement. This was tested through sixteen experimental conditions with different variations of a fake social media influencer post. Five dependent variables were tested, willingness to share the post, willingness to buy, attitude toward the brand, attitude towards the ad, and attitude towards the influencer. Four independent variables were also measured, size of the influencer (micro or macro), picture (present or not), discount (present or not), and level of purchase involvement (high or low), as well as several contributing variables about personality. The results contended that the presence of a picture in a social media influencers ad was had a positive effect on willingness to share the post, willingness to buy, attitude toward the brand, and attitude towards the ad. Discount also was significant to consumers’ attitudes towards the brand and the ad. Level of involvement and size of the influencer only proved to be statistically significant towards the effectiveness of the post when interaction effects were found between one or more of those variables. The research and analysis conducted will provide valuable information regarding the effectiveness of social media influencers and the relevance of them pertaining to technological shifts and advancements in the marketing field

The Pursuit of Conversion: Effects of Mediating Channels on Product Choices and Purchase Propensities in Social Commerce Platforms

This study elucidates the effectiveness of intermediary channels in driving sales at social commerce sites (SCSs). Using a panel data, we investigate how the external intermediary channels through which consumers arrive at SCSs influence product choice and purchase likelihood. In addition, we scrutinize the extent to which product categories with varying quality moderate the relationship between consumers’ channel-related behaviors and purchase propensities. Furthermore, we examine how external channels “collaborate” with internal channels to increase purchase likelihood. The findings suggest that consumers who enter the SCS through direct apps and portals engage in more proactive purchasing than do consumers landing at the SCS via metasites or e-mail promotions. Consumers who are directed to the SCS through metasites or e-mail promotions are more likely to purchase experience goods than search goods. Contrary to previous findings, consumers’ purchasing propensities decline, rather than increase, across all channels after the implementation of a recommendation system

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