A Product Recommendation Algorithm Based on Knapsack Optimization

Personalized Recommender System becomes an important research field in Electronic Commerce, and the main goal of current recommendation models is provide Best-Service to users. But, from enterprise’s viewpoint, the Max-Earning strategy is necessary to improve the benefit of enterprise. To solving this problem, knapsack model is applied to describe the commonly used Top-N recommend mechanism firstly. Then, the enterprise’s earnings are described as a constraint in knapsack model, a product recommended algorithm is proposed at the basis of optimization of knapsack problem. Experimental results show the proposed algorithm has similar performance with CF model when earning requirement and amount of recommended products is lower. So, both user’s value and enterprise’s value are improved through the proposed algorithm

HOTEL RECOMMENDATION SYSTEM BASED ON REVIEW AND CONTEXT INFORMATION: A COLLABORATIVE FILTERING APPRO

In most organizations, knowledge sharing is often lacking when it comes to business systems success. This paper investigates factors affecting business systems success in Saudi organisations. Data were collected from private organisations in Saudi Arabia and Partial Least Square approach has been applied to analyse the data. The results show that organisational culture influence knowledge sharing towards business systems success. In addition, both intrinsic motivation and perceived usefulness has positive influence on business system success. This indicates that business system success is built upon the concept of knowledge sharing and user motivation

SISTEM REKOMENDASI KULINER KHAS GRESIK MENGGUNAKAN METODE ITEM BASED COLLABORATIVE FILTERING

Setiap daerah memiliki ciri khasnya sendiri, seperti halnya di Kabupaten Gresik yang memiliki beraneka ragam kuliner yang menggugah selera. Kabupaten Gresik memiliki julukan lain terutama soal kuliner yakni Kota Pudak, Kota Bandeng, Kota Krawu dan Kota Sejuta Warung Kopi. Banyaknya kuliner yang ada di Kabupaten Gresik, membuat masyarakat merasa kebingungan dalam menentukan kuliner yang akan dinikmati, terlebih kuliner yang belum pernah dicoba sebelumnya. Pembangunan sebuah sistem rekomendasi kuliner menggunakan penerapan metode item based collaborative filtering dapat membantu dalam memberikan rekomendasi kuliner yang belum pernah dicoba sebelumnya. Keluaran yang dihasilkan dapat membantu masyarakat dalam menentukan kuliner yang akan dicoba menurut hasil rekomendasi yang dihasilkan

The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation

In movie/TV collaborative recommendation approaches, ratings users gave to already visited content are often used as the only input to build profiles. However, users might have rated equally the same movie but due to different reasons: either because of its genre, the crew or the director. In such cases, this rating is insufficient to represent in detail users’ preferences and it is wrong to conclude that they share similar tastes. The work presented in this paper tries to solve this ambiguity by exploiting hidden semantics in metadata elements. The influence of each of the standard description elements (actors, directors and genre) in representing user’s preferences is analyzed. Simulations were conducted using Movielens and Netflix datasets and different evaluation metrics were considered. The results demonstrate that the implemented approach yields significant advantages both in terms of improving performance, as well as in dealing with common limitations of standard collaborative algorithm.info:eu-repo/semantics/publishedVersio

ПОРІВНЯННЯ ТА ВИЗНАЧЕННЯ НЕДОЛІКІВ РОБОТИ АЛГОРИТМІВ РЕКОМЕНДАЦІЙНИХ СИСТЕМ

У статті проілюстровано застосування математичних методів для побудови рекомендаційних систем. Досліджено основні підходи до створення рекомендаційної системи. Розглянуті основні види та алгоритми побудови рекомендаційних систем. Проведено аналіз роботи алгоритмів, що дозволило виявити їх загальні недоліки та переваги.  Приділено увагу важливості машинного навчання і перераховані основні проблеми даного класу методів. Результати дослідження дають можливість оцінити точність, зрозумілість і корисність рекомендацій що лежить в основі для створення ідеальних рекомендаційних систем та сервісів

A doctor recommender system based on collaborative and content filtering

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity

THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

KONSEP MULTICRITERIA COLLABORATIVE FILTERING UNTUK PERBAIKAN REKOMENDASI

Untuk membantu pencari informasi yang belum memiliki referensi diperlukan alat bantu recommender system. Pengembangan recommender system sebagian besar dilakukan dengan menggunakan pendekatan berbasis collaborative filtering. Sistem berbasis collaborative filtering akan bekerja dengan cara mempelajari kebiasaan para pencari informasi dan membangun profil pencari informasi, kemudian memberikan rekomendasi. Pendekatan collaborative filtering klasik diterapkan pada kasus pemilihan item yang hanya memiliki satu kriteria. Sementara itu, banyak kasus yang tidak bisa dimodelkan dengan satu kriteria. Oleh karena itu konsep collaborative filtering perlu dikembangkan untuk pemilihan item yang memiliki banyak kriteria agar rekomendasi yang dihasilkan memiliki kualitas lebih baik dan relevan dengan kebutuhan pengguna