scholarly journals Multi-criteria based Item Recommendation Methods

Rekayasa ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 78-84
Author(s):  
Noor Ifada ◽  
Syafrurrizal Naridho ◽  
Mochammad Kautsar Sophan
Keyword(s):  
Collaborative Filtering ◽  
Empirical Analysis ◽  
Recommendation System ◽  
Cold Start ◽  
The Other ◽  
Other Hand ◽  
Rating Prediction ◽  
The Empirical Analysis

This paper comprehensively investigates and compares the performance of various multi-criteria based item recommendation methods. The development of the methods consists of three main phases: predicting rating per criterion; aggregating rating prediction of all criteria; and generating the top-  item recommendations. The multi-criteria based item recommendation methods are varied and labelled based on what approach is implemented to predict the rating per criterion, i.e., Collaborative Filtering (CF), Content-based (CB), and Hybrid. For the experiments, we generate two variations of datasets to represent the normal and cold-start conditions on the multi-criteria item recommendation system. The empirical analysis suggests that Hybrid and CF are best implemented on the normal and cold-start item conditions, respectively. On the other hand, CB should never be (solely) implemented in a multi-criteria based item recommendation system on any conditions.

Reply to Smith

1981 ◽  
Vol 11 (2) ◽  
pp. 247-248
Author(s):  
Willem E. Saris ◽  
Cees P. Middendorp
Keyword(s):  
Statistical Analysis ◽  
Empirical Analysis ◽  
The Other ◽  
Statistical Procedures ◽  
Other Hand ◽  
The Usa ◽  
The Empirical Analysis

Although we appreciate the attention the critic has given to our paper, we are somewhat disappointed about the kind of criticism. It is said that the ‘empirical analysis is fundamentally flawed’. But if the analysis is flawed it must be very easy to show it by a reanalysis of the data. However, if one takes the time to look at the data used in this study one can see immediately that when the USSR's level of armaments is very low the USA is producing large amounts of missiles. On the other hand, when the USSR has a large number of missiles the USA's production is nil or very little. Consequently one must conclude that the USA cannot possibly be reacting to the activities of the USSR in the simple ways suggested by Richardson or Hamblin et al. This result was confirmed by our statistical analysis of the data. One can of course try other statistical procedures, as we did, but they all produce the same result: there is no reaction effect in the USA's behaviour.

Populism or Nationalism? The ‘Paradoxical’ Non-Emergence of Populism in Cyprus

2021 ◽  
pp. 003232172198915
Author(s):  
Giorgos Venizelos
Keyword(s):  
Financial Crisis ◽  
Empirical Analysis ◽  
The Other ◽  
Nationalist Discourse ◽  
Other Hand ◽  
The People ◽  
National Question ◽  
The One ◽  
Necessary And Sufficient ◽  
The Empirical Analysis

This article investigates the curious non-emergence of populism in contemporary Cyprus despite the deep financial crisis and profound political disillusionment – conditions that are treated as necessary and sufficient. Putting emphasis on Cyprus’ key historical particularities, the article inquires into the ways Cyprus’ political past, and the subsequent salient ‘national question’, produce ambiguous notions of ‘the people’ on the one hand, and impede the potentials for a ‘populist moment’ on the other hand. By assessing the performative dynamics of oppositional parties in Cyprus, the empirical analysis suggests that the absence of populism is rooted in the following factors: First, nationalist discourse prevails over, and significantly weakens, populist discourse. Second, self-proclaimed challenger parties served ‘old wine in new bottles’ further undermining their position and claims. The failure of populism to take root in Cyprus, brings to the fore important theoretical insights relevant to the non-emergence of populism even under favourable conditions.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

Formenanalyse bei Marx und ihr Verhältnis zur Systemwissenschaft / Analysis of forms by Marx and its relation to systems research

1975 ◽  
Vol 4 (3) ◽  
Author(s):  
Werner Loh
Keyword(s):  
Mathematical Logic ◽  
Empirical Analysis ◽  
The Other ◽  
Systems Research ◽  
Other Hand ◽  
Empirical Logic ◽  
Formal Concepts ◽  
And Mathematics ◽  
Different Levels

AbstractMARX’s analysis of forms and modern systems research have in common the problem of form. MARX analyzed forms by functionally relating elements to each other on different levels. Contrary to modern systems theories and Marxism-Leninism elements are for MARX forms themselves and not non-formal elementary qualities. The analysis of forms, therefore, is able to characterize its objects only relationally-functionally. On the other hand modern systems theories integrate concepts like ‚action‘ or ‚goal‘ in an elementaristic manner. The analysis of forms must be controlled by systematic concretization and totalization adequate to the problem. The formal concepts of systems research are often interpreted as logical-mathematical. Logic and mathematics are usually understood as non-empirical. Empirical analysis of forms is in need of an empirical logic and mathematics.

scholarly journals Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System

Rekayasa ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 234-239
Author(s):  
Noor Ifada ◽  
Nur Fitriani Dwi Putri ◽  
Mochammad Kautsar Sophan
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Method Development ◽  
Recommendation System ◽  
Rating Data ◽  
The Real ◽  
Rating Prediction ◽  
Filtering Approach ◽  
Better Than

A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy.  Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most  in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.

scholarly journals TEKNOLOGI DAN PERTUMBUHAN EKONOMI

2007 ◽  
Vol 2 (2) ◽  
pp. 161
Author(s):  
Eni Setyowati
Keyword(s):  
Total Factor Productivity ◽  
Empirical Analysis ◽  
Technology Innovation ◽  
The Other ◽  
Factor Productivity ◽  
Development Technology ◽  
National Production ◽  
Capital Development ◽  
The Empirical Analysis ◽  
Capital Intensive

People efficiently aware that exploitation of development technology gives significant economy advantage. This such of awareness supports the development of competition in technology innovation and the competition of exploitation technology to reach bigger economy advantage. The economical impact of the exploitation of technology constitutes occurring of management and organization transition in various companies both of a capital intensive and labour intensive. The writer also analyses an opinion of neoclassic economist about advancement of technology. The empirical analysis points out that national production (Y) is not only caused by capital development (K) and the growth of employee (L), but also caused by the other factor, which at the beginning are considered as residual factor. It is called Total Factor Productivity (TFP).

Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++

2021 ◽  
pp. 1-19
Author(s):  
Taushif Anwar ◽  
V. Uma ◽  
Gautam Srivastava
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Error Rate ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Matrix Decomposition ◽  
Cold Start ◽  
Singular Value ◽  
Decomposition Approach ◽  
Value Decomposition

In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start. The matrix decomposition approach is demonstrated to be one of the effective approaches used in developing recommendation systems. This paper presents a new approach that uses CF and Singular Value Decomposition (SVD)[Formula: see text] for implementing a recommendation system. Therefore, this work is an attempt to extend the existing recommendation systems by (i) finding similarity between user and item from rating matrices using cosine similarity; (ii) predicting missing ratings using a matrix decomposition approach, and (iii) recommending top-N user-preferred items. The recommender system’s performance is evaluated considering Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Performance evaluation is accomplished by comparing the systems developed using CF in combination with six different algorithms, namely SVD, SVD[Formula: see text], Co-Clustering, KNNBasic, KNNBaseline, and KNNWithMeans. We have experimented using MovieLens 100[Formula: see text]K, MovieLens 1[Formula: see text]M, and BookCrossing datasets. The results prove that the proposed approach gives a lesser error rate when cross-validation ([Formula: see text]) is performed. The experimental results show that the lowest error rate is achieved with MovieLens 100[Formula: see text]K dataset ([Formula: see text], [Formula: see text]). The proposed approach also alleviates the sparsity and cold-start problems and recommends the relevant items.

A Personalized Recommender Systems Framework Based on Ontology and Bayesian Network in E-Commerce

2010 ◽  
Vol 143-144 ◽  
pp. 961-965
Author(s):  
Fei Long ◽  
Yu Feng Zhang ◽  
Feng Hu
Keyword(s):  
Bayesian Network ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Cold Start ◽  
Logical Reasoning ◽  
The Other ◽  
Personalized Recommendation ◽  
Reasoning Under Uncertainty ◽  
The One ◽  
Systems Framework

Personalized recommendation methods are mainly classified into content-based recommendation approach and collaborative filtering recommendation approach. However, Both recommendation approaches have their own drawbacks such as sparsity, cold-start and scalability. To overcome the drawbacks, In this paper, we propose a framework for recommender systems that join use of Ontology and Bayesian Network. On the one hand, Ontology help formally defining the semantics of variables included in the Bayesian network, thus allowing logical reasoning on them. On the other hand, Bayesian network allow reasoning under uncertainty, that is not possible only with the use of ontology. In the recommendation, products not yet purchased or rarely purchased can still be recommended to customers with accuracy.

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