Review on “Typicality-Based Collaborative Filtering Recommendation using Sub Clustering for Online Shopping”

Collaborative filtering is a convenient mechanism used in recommender system, which is used to find the similar items in a group. The same favour items can be identified by using the collaborative filtering based on items and the users. However there are some drawbacks in premature filtering techniques which lead to less accuracy, data sparsity and prediction errors. In this work take advantage of proposal of object typicality from cognitive psychology moreover suggests a typicality-based collaborative filtering recommendation method named as Tyco. A distinguishing characteristic of typicality-based collaborative filtering is that it finds neighbours of users on the basis of user typicality degrees in user groups. Selection of neighbours regarding users by means of measuring users’ similarity on the basis of their typicality degrees is a separate feature, which distinguishes this approach from earlier collaborative filtering methods. It exceeds many CF recommendation methods on recommendation accuracy on any type of datasets. In proposed method main approach is to Sub Clusters the all items into several item groups by applying such as nearest neighboring algorithm. This helps users to search items more easily and to increase the accuracy and quality of the recommendation

Combination of a Cluster-Based and Content-Based Collaborative Filtering Approach for Recommender System

With the development in technology in the field of e-commerce, the problem with information overload has been at its peak. Oftentimes the user is overwhelmed by the huge amount of options he/she is provided with while searching for an item. This is when recommender system comes in handy, which is an information filtering technique aimed at presenting the user with the most possible options based on certain reference characteristics. However, the problem with many recommender systems is that they are associated with a high cost of learning customer preferences. The current agricultural web application uses recommendation system along with the collaborative filtering concept which introduces the Agricultural Informative System (AIS) that uses pseudo feedback, which provides a method for automatic local analysis about the user preferences with the help of clustering in collaborative filtering. The AIS uses pseudo feedback to capture the preferences which are stored in the users profile for future personalized recommendations to address the problem. DOI: 10.17762/ijritcc2321-8169.15078

A Survey on Review Based Recommendation System

The advances in internet technology have resulted in the generation of huge amount of data called as Big Data. Recommendation system is a widely used technique for the filtering the huge amount of data and providing recommendations to users according to their interest. Without taking previous user interest into consideration, the traditional recommender system does not provide efficient solutions to the users. In this paper, we introduce recommender system to solve the above-described problems. The proposed recommender system will take into consideration previous user’s interest and active user interest and by calculating similarity it will to provide recommendations to active user

Product Recommendation using Hadoop

Recommendation systems are used widely to provide personalized recommendations to users. Such systems are used by e-commerce and social networking websites to increase their business and user engagement. Day-to-day growth of customers and products pose a challenge for generating high quality recommendations. Moreover, they are even needed to perform many recommendations per second, for millions of customers and products. In such scenarios, implementing a recommendation algorithm sequentially has large performance issues. To address such issues, we propose a parallel algorithm to generate recommendations by using Hadoop map-reduce framework. In this implementation, we will focus on item-based collaborative filtering technique based on user's browsing history, which is a well-known technique to generate recommendations

Time Based Collaborative Recommendation System by using Data Mining Techniques

Recommendation of appropriate product to the specific user is becoming the key to ensuring the continued success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique and Structural Balance Theory-based Recommendation (i.e., SBT-Rec) technique to realize product item recommendation. Overall, the present CF recommendation and as per suggested SBT can perform very well, if the target user owns similar friends (user-based CF) and Structural Balance Theory-based Recommendation (i.e., SBT-Rec) for we first look for the target user’s dissimilar “enemy” (i.e., antonym of “friend”), and furthermore, we look for the “possible friends” of E-commerce target user, according to “enemy’s enemy is a friend” rule of Structural Balance Theory or the product items purchased and preferred by target user own one or more similar product items (item-based CF). Here both the systems depends on friends and enemies if we are not getting friends or enemies then. So to improve Recommender system we propose a time-aware profile based collaborative Recommendation algorithm. In this algorithm, we will consider only recently submitted ratings and positive reviews to evaluate products quality. Along with this, we propose a novel recommender system in which user will give his requirement about any product as input, and depending on that input we will recommend most appropriate products according to the customer’s requirement and ratings given by other customers. Only recent ratings will be considered by the system. Our proposed system will meet personalized product item recommendation requirements in E-commerce and time-aware rating consideration to evaluate current product quality

Rancang Bangun Sistem Rekomendasi Tempat Makan Menggunakan Algoritma Typicality Based Collaborative Filtering

Makanan merupakan bagian penting bagi manusia baik sebagai kebutuhan primer maupun sebagai bagian dari gaya hidup seseorang. Tempat makan yang semakin banyak dan tawaran makanan yang beragam, membuat seseorang memiliki lebih banyak pilihan akan tempat makan yang dapat dikunjungi. Dengan dibantu oleh teknologi, sekarang seseorang bisa mencari rekomendasi dengan menggunakan algoritma sistem rekomendasi. Saat ini ada tiga algoritma sistem rekomendasi yang populer yaitu Content Based Filtering (CBF), Collaborative Filtering (CF) dan metode hybrid. Metode CF merekomendasikan sebuah item kepada pengguna dengan memprediksi preferensi dari pengguna aktif terhadap sejumlah item berdasarkan preferensi dari pengguna atau item lain yang mirip. Ada dua jenis metode dalam CF yaitu User Based CF dan Item Based CF. Terdapat sebuah metode baru yang dikembangkan dari metode User Based CF, metode ini adalah Typicality Based CF. Typicality Based CF (TyCo) memiliki kelebihan yang tidak dimiliki metode CF lainnya dapat memberikan prediksi yang akurat walau data terbatas, dapat melakukan clustering tanpa algoritma tambahan dan dapat mengatasi masalah cold-start yang biasa dialami metode CF. Berdasarkan pengujian yang telah dilakukan diketahui bahwa aplikasi ini memiliki nilai rata-rata Mean Absolute Error (MAE) sebesar 1.366 yang disebabkan karena kurangnya data training

Sistem Rekomendasi Wisata Kuliner di Yogyakarta dengan Metode Item-Based Collaborative Filtering

Sistem rekomendasi adalah sistem yang mampu memberikan rekomendasi item-item yang mungkin disukai oleh pengguna. Metode Collaborative Filtering merupakan salah satu metode pada sistem rekomendasi. Metode ini memanfaatkan penilaian pengguna berupa rating untuk memprediksi item yang mungkin diminati. Berdasarkan rating pengguna dari 1 - 5, nilai kemiripan dihitung menggunakan adjusted cosine similarity. Berdasarkan nilai kemiripan antar makanan, nilai prediksi rating makanan dicari menggunakan weighted sum. Penelitian ini menggunakan 23 makanan dan 22 pengguna sebagai data. Dalam mengimplementasikan metode item - based collaborative filtering, penulis melakukan metode pengumpulan data, perancangan tampilan, melakukan perhitungan manual, pembangunan sistem dan implementasi metode item - based collaborative filtering, melakukan pengujian MAE, pengujian Confusion Matrix, dan pengujian F1 Score. Dari hasil pengujian yang telah dilakukan diperoleh prediksi yang cukup akurat dengan 6 neighbor dan akurasi 83 %

Penerapan Metode Collaborative Filtering Dan Knowledge Item Based Terhadap Sistem Rekomendasi Kamera DSLR

Sistem rekomendasi adalah sistem yang dibuat dengan tujuan untuk membantu pengguna dalam mengetahui item yang diminati oleh mereka. Sistem rekomendasi banyak diimplementasikan di marketplace, sosial media dan untuk tujuan lainnya. Salah satu proses yang membutuhkan sistem rekomendasi adalah pada proses pemilihan kamera. Pemilihan kamera untuk fotografer yang belum berpengalaman menggunakan kamera menjadi salah satu permasalahan yang cukup penting dikarenakan banyaknya kamera yang bermunculan hingga saat ini. Proses pemilihan kamera biasanya dilakukan dengan bertanya kepada fotografer senior yang sudah terjun lama dalam bidang fotografi agar diberikan rekomendasi terkait kamera yang sesuai dengan kriteria. Proses konvensional tersebut tentunya akan memakan waktu yang sangat lama. Oleh karena permasalahan tersebut, maka perlu dilakukan penelitian untuk sebuah sistem informasi rekomendasi pada proses pemilihan kamera. Pada penelitian ini akan diterapkan 2 metode rekomendasi yaitu metode Collaborative Filtering dan Knowledge Item Based. Hasil penelitian menunjukkan bahwa sistem informasi rekomendasi kamera DSLR yang dibangun menerapkan metode Collaborative Filtering dan Knowledge Item Based dalam memberikan rekomendasi prediksi pilihan kamera berdasarkan pola rating dari user lainnya

Time Based Collaborative Recommendation System by using Data Mining Techniques

Recommendation of appropriate product to the specific user is becoming the key to ensuring the continued success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique and Structural Balance Theory-based Recommendation (i.e., SBT-Rec) technique to realize product item recommendation. Overall, the present CF recommendation and as per suggested SBT can perform very well, if the target user owns similar friends (user-based CF) and Structural Balance Theory-based Recommendation (i.e., SBT-Rec) for we first look for the target user’s dissimilar “enemy” (i.e., antonym of “friend”), and furthermore, we look for the “possible friends” of E-commerce target user, according to “enemy’s enemy is a friend” rule of Structural Balance Theory or the product items purchased and preferred by target user own one or more similar product items (item-based CF). Here both the systems depends on friends and enemies if we are not getting friends or enemies then. So to improve Recommender system we propose a time-aware profile based collaborative Recommendation algorithm. In this algorithm, we will consider only recently submitted ratings and positive reviews to evaluate products quality. Along with this, we propose a novel recommender system in which user will give his requirement about any product as input, and depending on that input we will recommend most appropriate products according to the customer’s requirement and ratings given by other customers. Only recent ratings will be considered by the system. Our proposed system will meet personalized product item recommendation requirements in E-commerce and time-aware rating consideration to evaluate current product quality