Improving Video Game Recommendations Using a Hybrid, Neural Network and Keyword Ranking Approach

Recommendations systems are software solutions for finding high-quality and relevant content for a given user type ranging from online shoppers, to music listeners, to video game players. Traditional recommendation systems use user review data to make recommendations, but we still want recommendations to perform well for new users with no review data. Currently, one of the problems that exists in recommendations is poor recommendation accuracy when only a small amount of data exists for a user, called the cold start problem. In this research we investigate solutions for the cold start problem in video game recommendations and we propose a solution that uses a hybrid neural network and keyword ranking approach. We evaluate this system with precision and recall metrics and compare the results to a traditional recommendation system. We present that this hybrid system offers performance gains when recommending to users who have low-medium previous reviews

Finding the hidden gems: recommending untagged music.

We have developed a novel hybrid representation for Music Information Retrieval. Our representation is built by incorporating audio content into the tag space in a tag-track matrix, and then learning hybrid concepts using latent semantic analysis. We apply this representation to the task of music recommendation, using similarity-based retrieval from a query music track. We also develop a new approach to evaluating music recommender systems, which is based upon the relationship of users liking tracks. We are interested in measuring the recommendation quality, and the rate at which cold-start tracks are recommended. Our hybrid representation is able to outperform a tag-only representation, in terms of both recommendation quality and the rate that cold-start tracks are included as recommendations

A Deep Learning Approach towards Cold Start Problem in Movie Recommendation System

Recommendation systems play an important role for e-commerce websites to make profits. It has a variety of applications in different domains. There are three types of categories in which recommendation systems are classified i.e. content based, collaborative and hybrid systems. These systems suffer when a redundant amount of information is not available to provide recommendations. This problem is known as the cold start problem. In this digital era, it is possible to collect meta information about a user and provide rich recommendations. Various approaches such as social media analysis, graph networks have been proposed to solve this problem. But they lack personalization and generate irrelevant recommendations affecting the system performance. The objective of this work is to resolve new user cold start problem in movie recommendation systems using a deep learning approach that utilizes demographic attributes to cluster similar users. This embedding is given to the deep neural network to generate the recommendations. From the analysis done, we verify the effectiveness of our approach.

Resolving Cold Start Problem Using User Demographics and Machine Learning Techniques for Movie Recommender Systems

There is a substantial increase in demand for recommender systems which have applications in a variety of domains. The goal of recommendations is to provide relevant choices to users. In practice, there are multiple methodologies in which recommendations take place like Collaborative Filtering (CF), Content-based filtering and Hybrid approach. For this paper, we will consider these approaches to be traditional approaches. The advantages of these approaches are in their design, functionality and efficiency. However, they do suffer from some major problems such as data sparsity, scalability and cold start to name a few. Among these problems, cold start is an intriguing area which has been plaguing recommender systems. Cold start problem occurs when the recommender system is not able to recommend new users/items since there is data sparsity. Researchers have formulated innovative techniques to alleviate cold start and the existing research conducted in this area is tremendous since the problem materializes in different use cases. Cold start is categorized into three problems. The first problem is when new users needs product recommendations from the system. The second problem is when new products listed in the system need to be recommended to existing users. The last problem is when new users and new products are present and the recommender engine needs to generate relevant recommendations. In this thesis, we concentrate on the first problem, where a user who is completely new to the system needs quality recommendations. We use a movie recommendation platform as our use case to analyze user demographics and find similarities between existing and new users to produce relevant recommendations

A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features

Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering. Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and its accuracy improves as its pool of users expands. However, the weakness with this approach is the Cold Start problem. It is difficult to recommend items that are either brand new or have no user activity. Systems that use content-based filtering recommend items based on extracted information from the actual content. A popular example of this approach is Pandora Internet Radio. This approach overcomes the Cold Start problem. However, the main issue with this approach is its heavy demand on computational power. Also, the semantic meaning of an item may not be taken into account when producing recommendations. In this thesis, a hybrid approach is proposed by utilizing the strengths of both collaborative and content-based filtering techniques. As proof-of-concept, a hybrid music recommendation system was developed and evaluated by users. The results show that this system effectively tackles the Cold Start problem and provides more variation on what is recommended

An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering

Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender systems recommend items by identifying other users with similar taste and use their opinions for recommendation; whereas content-based recommender systems recommend items based on the content information of the items. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed a unique switching hybrid recommendation approach by combining a Naive Bayes classification approach with the collaborative filtering. Experimental results on two different data sets, show that the proposed algorithm is scalable and provide better performance – in terms of accuracy and coverage – than other algorithms while at the same time eliminates some recorded problems with the recommender systems

Recommender System using Collaborative Filtering and Demographic Characteristics of Users

Recommender systems use variety of data mining techniques and algorithms to identify relevant preferences of items for users in a system out of available millions of choices. Recommender systems are classified into Collaborative filtering, Content-Based filtering, Knowledge-Based filtering and Hybrid filtering systems. The traditional recommender systems approaches are facing many challenges like data sparsity, cold start problem, scalability, synonymy, shilling attacks, gray sheep and black sheep problems. These problems consequently degrade the performance of recommender systems to a great extent. Among these cold start problem is one of the challenges which comes into scene when either a new user enters into a system or a new product arrives in catalogue. Both situations lead to difficulty in predicting user preferences due to non-availability of sufficient user rating history. The study proposes a new hybrid recommender system framework for solving new user cold-start problem by exploiting user demographic characteristics for finding similarity between new user and already existing users in the system. The efficiency of recommender systems can be improved by proposed approach which calculates recommendations for new user by predicting preferences within much smaller cluster rather than from the entire customer base. The analysis has been done using MovieLens dataset for enhancing the performance of online movie recommendation system. DOI: 10.17762/ijritcc2321-8169.15077

The intellectual system of movies recommendations based on the collaborative filtering

The investigation deals with designing and developing of intellectual system of movies recommendations  based on the collaborative filtering using the Python software environment. In particular, the approaches (Content-based approach, Collaborative filtering, Hybrid models) in recommendatory system construction with the help of neural networks have been analyzed. It has been established that it is difficult to implement and learn the Content-based approach and it strongly depends on the subject area. Collaborative filtering is more simple in implementation, training, it is universal, but it has a flaw in the form of a «cold-start». Accordingly, the collaborative filtering has been chosen for the design and development of the intellectual system of movies recommendations. While designing a system of recommendations based on collaborative filtering, the Naive Recommendations, Recommendations based on average ratings of similar users, Recommendations based on average user ratings and similarity matrix have been described; their algorithm and their implementation using the Python software environment have been demonstrated. As a result the intellectual system of recommendations has been realized and it can offer a movie to the user according to his/her preferences

Algoritmo Híbrido de Recomendação

Nesta era tecnológica em que nos encontramos há cada vez mais informação disponível na internet, mas grande parte dessa informação não é relevante. Isto leva à necessidade de criar maneiras de filtrar informação, de forma a reduzir o tempo de recolha de informação útil. Esta necessidade torna o uso de sistemas de recomendação muito apelativo, visto estes personalizarem as pesquisas de forma a ajudar os seus utilizadores a fazer escolhas mais informadas. Os sistemas de recomendação procuram recomendar os itens mais relevantes aos seus utilizadores, no entanto necessitam de informação sobre os utilizadores e os itens, de forma a melhor os poder organizar e categorizar. Há vários tipos de sistemas de recomendação, cada um com as suas forças e fraquezas. De modo a superar as limitações destes sistemas surgiram os sistemas de recomendação híbridos, que procuram combinar características dos diferentes tipos de sistemas de recomendação de modo a reduzir, ou eliminar, as suas fraquezas. Uma das limitações dos sistemas de recomendação acontece quando o próprio sistema não tem informação suficiente para fazer recomendações. Esta limitação tem o nome de Cold Start e pode focar-se numa de duas áreas: quando a falta de informação vem do utilizador, conhecida como User Cold Start; e quando a falta de informação vem de um item, conhecida como Item Cold Start. O foco desta dissertação é no User Cold Start, nomeadamente na criação de um sistema de recomendação híbrido capaz de lidar com esta situação. A abordagem apresentada nesta dissertação procura combinar a segmentação de clientes com regras de associação. O objetivo passa por descobrir os utilizadores mais similares aos utilizadores numa situação de Cold Start e, através dos itens avaliados pelos utilizadores mais similares, recomendar os itens considerados mais relevantes, obtidos através de regras de associação. O algoritmo híbrido apresentado nesta dissertação procura e classifica todos os tipos de utilizadores. Quando um utilizador numa situação de Cold Start está à procura de recomendações, o sistema encontra itens para recomendar através da aplicação de regras de associação a itens avaliados por utilizadores no mesmo grupo que o utilizador na situação de Cold Start, cruzando essas regras com os itens avaliados por este último e apresentando as recomendações com base no resultado.Recommender systems, or recommenders, are a way to filter the useful information from the data, in this age where there is a lot of available data. A recommender system’s purpose is to recommend relevant items to users, and to do that, it requires information on both, data from users and from items, to better organise and categorise both of them. There are several types of recommenders, each best suited for a specific purpose, and with specific weaknesses. Then there are hybrid recommenders, made by combining one or more types of recommenders in a way that each type supresses, or at least limits, the weaknesses of the other types. A very important weakness of recommender systems occurs when the system doesn’t have enough information about something and so, it cannot make a recommendation. This problem known as a Cold Start problem is addressed in this thesis. There are two types of Cold Start problems: those where the lack of information comes from a user (User Cold Start) and those where it comes from an item (Item Cold Start). This thesis’ main focus is on User Cold Start problems. A novel approach is introduced in this thesis which combines clients’ segmentation with association rules. The goal is first, finding the most similar users to cold start users and then, with the items rated by these similar users, recommend those that are most suitable, which are gotten through association rules. The hybrid algorithm presented in this thesis finds and classifies all users’ types. When a user in a Cold Start situation is looking for recommendations, the system finds the items to recommend to him by applying association rules to the items evaluated by users in the same user group as the Cold Start user, crossing them with the few items evaluated by the Cold Start user and finally making its recommendations based on that