Recommendation System Based on Item and User Similarity on Restaurants Directory Online
The growing number of internet companies are demanding the company to innovate through technology. This is also applied to restaurant directory companies, they should give recommendation of restaurant which suit best on customer needs. This study aims to develop a system to provide recommendation for customer in restaurant selection. We merge the item similarity and user similarity features to generaterecommendations. Evaluation shows that the recommendation system based on item similarity yields higher F1-measure value when comparing to user similarity.
Today, the internet is becoming a technology that has been used in various companies. This technology provides an opportunity for companies to be able to innovate in developing their services [5]. One country that has many internet companies that are growing is Indonesia. It is marked by the existence of internet companies that have become big companies and known by the community because the services they have are able to provide a huge added value such asGojek, Tokopedia, and Traveloka. In addition to these companies, there are many more internet companies that are developing in Indonesia. One of them is a company that provides restaurant directoryonline such asZomato, PergiKuliner, and Qraved. The growing number of internet companies demands that they constantly innovate by developing their own technology. It aims to increase the number of users and lead to increase their revenue. One of the technology that can be developed is the recommendation system because it has become an indispensable component in the world of e-commerce [11] and is the key to success in businesses that utilize internet services [4].The recommendation system is a system that aims to filter amount of information that will generate recommendations dynamically based on user preferences, interests, or behavior of an item [7 ] and most appealing to a particular user [10]. The basic idea of a recommendation is to utilize multiple data sources to attract users [1]. The purpose of the recommendation system is to assist the user in making a decision [2] and predict which items will be chosen by users based on their preferences [7 ]. Ricci et al [10] mentioned that the recommendation system has an important role in well-known internet companies such as Amazon, YouTube, Netflix, Spotify, LinkedIn, Facebook, TripAdvisor, Last.fm and IMDb. There are a number of benefits when a company has a recommendation system such as increasing the number of items sold, selling more diversified goods, increasing customer satisfaction, increasing user loyalty, and better understanding the desires of users.According to Isinkaye, Folajimi, and Ojokoh [7], there are 3 approaches that can be used in generating recommendations: collaborative filtering, content-based filtering, and hybrid filtering. One of the best approaches is hybrid filtering because hybrid filtering is a combination of several approaches that aim to cover the shortcomings of each approach. These deficiencies include: cold-start problem, sparsity problem data, scalability, and synonyms. This research aims to generate recommendation system using hybrid filtering by combine features of collaborative filtering and content-based filtering.The rest of the paper is organized as follows. The next section, we describe the related work regarding the topics, our proposed system is described in the third section. Fourth section explains the data and experiment setting, while the results and discussions of experiment are described in fifth section. Lastly, in the six section, we describe conclusions and future works.
This research produced a recommendation system based on item and user similarity. The approach is built by combining features derived from content based filtering and collaborative filtering approaches. The merging of these features is used to generate two models that will be used by the system to generate a set of recommendations.Evaluation conducted on both systems shows that the system built by utilizing the similarity value of the restaurant is able to produce a better f1-measure value. Based on the amount of data used in the construction of a model, the evaluation also shows that models built over a long time span are able to produce better F1-measure values. It can be concluded that the more incoming reviews data, the better the recommendations received by the target users. Based on the recommendations generated, the system is able to recommend restaurants that do not have reviews yet. This can help users who want to visit a new restaurant or do not have a review yet.For further research, it is expected to consider several features that can be used in building a model. These features include: the number of user’s review, the number of restaurant’s reviews, the average value a user gives in a review, or the average grade that a restaurant obtains from its service users. These features are expected to produce a better f1-measure value.One of the challenges in the recommendation system is the new user who has not provided a review on the online restaurant directory service. To address these challenges, further research can consider user behavior when users view certain restaurant pages or use cookies data stored in web applications. Hopefully, the data can replace the rating feature used in the collaborative filtering approach