Recommendation System for News Reader

Recommendation Systems help users to find information and make decisions where they lack the required knowledge to judge a particular product. Also, the information dataset available can be huge and recommendation systems help in filtering this data according to users‟ needs. Recommendation systems can be used in various different ways to facilitate its users with effective information sorting. For a person who loves reading, this paper presents the research and implementation of a Recommendation System for a NewsReader Application using Android Platform. The NewsReader Application proactively recommends news articles as per the reading habits of the user, recorded over a period of time and also recommends the currently trending articles. Recommendation systems and their implementations using various algorithms is the primary area of study for this project. This research paper compares and details popular recommendation algorithms viz. Content based recommendation systems, Collaborative recommendation systems etc. Moreover, it also presents a more efficient Hybrid approach that absorbs the best aspects from both the algorithms mentioned above, while trying to eliminate all the potential drawbacks observed

Relationship based Entity Recommendation System

With the increase in usage of the internet as a place to search for information, the importance of the level of relevance of the results returned by search engines have increased by many folds in recent years. In this paper, we propose techniques to improve the relevance of results shown by a search engine, by using the kinds of relationships between entities a user is interested in. We propose a technique that uses relationships between entities to recommend related entities from a knowledge base which is a collection of entities and the relationships with which they are connected to other entities. These relationships depict more real world relationships between entities, rather than just simple “is-a” or “has-a” relationships. The system keeps track of relationships on which user is clicking and uses this click count as a preference indicator to recommend future entities. This approach is very useful in modern day semantic web searches for recommending entities of user’s interests

Intent-Aware Contextual Recommendation System

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining (ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field cannot be longer than 1,920 characters," the abstract appearing here is slightly shorter than the one in the PDF fil

Reciprocal Recommendation System for Online Dating

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line

Recommendation System for Vocational Major Streaming by C4.5 Algorithm

This study was aimed at presenting decision tree model using C4.5 algorithm in developing a major selection system for vocational schools. The study was reseach and development using questionnaires and documentation as data collection instruments. The input variables were: interest, academic talent, National Exam score, and gender. The target variable was choice of majors. Decision trees were used to analyze the data from grade 10 of vocational schools Batang in District. The C4.5 Algorithm was used to build decision trees in describing the relationship between the input variables and the target variable in the form of patterns. The patterns were used as a guide for the classification of the input variables into the target variable. The data were analyzed by comparing results of the output system and students' highest parallel ranking. Results show that the system is able to provide appropriate recommendations up to 83.33% out of the 48 tested dataSISTEM REKOMENDASI PENJURUSAN SEKOLAH MENENGAH KEJURUAN DENGAN ALGORITMA C4.5Penelitian ini bertujuan untuk menyajikan model decision tree dengan algoritma C4.5 dalam mengembangkan sistem rekomendasi pemilihan jurusan untuk calon siswa baru Sekolah Menengah Kejuruan (SMK). Pendekatan yang digunakan adalah research and development (R&D). Pengumpulan data dilakukan dengan teknik angket dan studi dokumentasi. Variabel input yang digunakan dalam penelitian ini antara lain: minat, bakat akademik, nilai ujian nasional, dan jenis kelamin. Pilihan jurusan menjadi variabel target. Decision tree digunakan dalam menganalisis data siswa kelas 10 SMK se-Kecamatan Batang. Algoritma C4.5 digunakan untuk membangun decision tree yang menggambarkan hubungan antara variabel input dengan variabel target dalam bentuk pola. Pola tersebut digunakan sebagai aturan untuk proses klasifikasi variabel input ke dalam variabel target. Data penelitian dianalisis dengan cara membandingkan hasil output system dengan data siswa kelas 10 dengan tiga besar ranking paralel sebagai data uji. Hasil uji sistem menunjukkan bahwa sistem dapat memberikan rekomendasi yang tepat sebesar 83,33% dari 48 data uj