Incorporating group recommendations to recommender systems: Alternatives and performance

Abstract

Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items.Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. VIII IX ABSTRACTThe importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things.As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In thisPhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation.I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present.The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets. The kinds of filtering most used at the beginning of the RS (collaborative, content-based and demographic) were described in Pazzani (1999). Breese,

Group Recommendation Systems Based on External Social‐Trust Networks

“…Group recommendation systems could be classified into two main categories [12,13]: those which perform an aggregation of individuals' preferences to obtain a possible group evaluation for each candidate item and those which perform an aggregation of individuals' models into a single group model and generate suggestions based on this model. In the first method, an individual-based recommendation system is first used to generate recommendations for each group member, then a group consensus function is used to merge the individual recommendations and select ones that are most suitable for the whole group.…”

A Group Recommendation Algorithm Based on Group Members' Weights and Project Popularity

“…More recently, many scholars have conducted in-depth research on how to improve the effect of group recommendation, and put forward many related algorithms. Chao et al [8] the behavior of the nearest neighbors. But the method does not consider the role of the user in the group, which affects the quality of the recommendations.…”

“…Many scholars focused on existing and non-existing group recommendations, user interest modeling, group rating prediction accuracy and recommendation accuracy and other issues. They have put forward some recommendation algorithms and designed some experimental prototype system [5,6,7,8,9,10] . But much of the work does not take into consideration in the differences in the level of concern of different groups for each category of project, as well as differences in the level of concern among members of the same group for each category of item.…”

Improved Collaborative Filtering Algorithm Using Topic Model

“…The experiment result is shown in Figure 4. The x-axis of Figure 4 is neighbour size, y-axis is MAE calculated by Equation (10). There are 5 curve in Figure 4,which reprents MAE of different cluster.…”