Road Trip Recommendation System Using User Preferences

2021 ◽  
Author(s):  
Kavitha P
Keyword(s):  
Recommendation System ◽  
User Preferences ◽  
Road Trip

scholarly journals Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

2020 ◽  
Vol 2 (95) ◽  
pp. 21-27
Author(s):  
S. F. Chalyi ◽  
V. O. Leshchynskyi
Keyword(s):  
Recommendation System ◽  
Selection Process ◽  
Temporal Patterns ◽  
User Preferences ◽  
Data Sets ◽  
Goods And Services ◽  
Temporal Relationships ◽  
Sequential Selection ◽  
System User ◽  
Alternative Choice

The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

A Survey on Recent Recommendation Systems for the Tourism Industry

2020 ◽  
pp. 205-237
Author(s):  
S. Ranjith ◽  
P. Victer Paul
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Tourism Industry ◽  
User Preferences ◽  
Reference List ◽  
Current State ◽  
Recent Recommendation ◽  
Important Field ◽  
Preference Criteria

Data mining is an important field that derives insights from the data and recommendation systems. Recommendation systems have become common in recent years in the field of tourism. These are widely used as a tool that can input various selection criteria and user preferences and yields travel recommendations to tourists. User's style and preferences should be constructed accurately so as to supply most relevant suggestions. Researchers proposed various types of tourism recommendation systems (TRS) in order to improve the accuracy and user satisfaction. In this chapter, the authors studied the current state of tourism recommendation system models and discussed their preference criteria. As a part of that, the authors studied various important preference factors in TRS and categorized them based on their likeness. This chapter reports TRS model future directions and compiles a comprehensive reference list to assist researchers.

Research on Modeling User’s Preference in the Steel E-Trading Platform

2015 ◽  
Vol 743 ◽  
pp. 687-691
Author(s):  
Ping Wu ◽  
Tao Yu ◽  
J.B. Du ◽  
G.Q. Qu ◽  
Feng Xiong
Keyword(s):  
Recommendation System ◽  
User Preferences ◽  
Cluster Method ◽  
Basic Information ◽  
Short Term

In order to meet the increasing personalized needs of users in the steel trading platform, the intelligent recommendation system has been introduced into the platform. And the users’ interests and preferences-based modeling is the key and foundation of recommendation system, and changes with the change of time. So, in this paper, the user preferences are divided into long-term and short-term firstly, then the users’ basic information vectors and cluster method are used to model users’ long-term interests and preferences, while mining and analyzing users’ operating records in the platform to model users’ the short-term. Finally, the whole interest and preference’s model of user will be built by integrating the two models.

scholarly journals Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

2019 ◽  
Vol 12 (2) ◽  
pp. 85
Author(s):  
Ratih Kartika Dewi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Recommendation System ◽  
Group Decision ◽  
User Preferences ◽  
Group Decision Support ◽  
Group Decision Support System ◽  
Topsis Method ◽  
User Groups

This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendation

scholarly journals Hybrid POI Recommendation System for Tourism

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Monishkanna Barathan ◽  
Ershad Sharifahmadian
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Interaction ◽  
User Preferences ◽  
Knowledge Based ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Information Finding ◽  
Hybrid Recommender ◽  
Available Information

Due to the increase in amount of available information, finding places and planning of the activities to be done during a tour can be strenuous. Tourists are looking for information about a place in which they have not been before, which worsen the selection of places that fit better with user’s preferences. Recommendation systems have been fundamentally applicable in tourism, suggest suitable places, and effectively prune large information from different locations, so tourists are directed toward those places where are matched with their needs and preferences. Several techniques have been studied for point-of-interest (POI) recommendation, including content-based which builds based on user preferences, collaborative filtering which exploits the behavior of other users, and different places, knowledge-based method, and several other techniques. These methods are vulnerable to some limitations and shortcomings related to recommendation environment such as scalability, sparsity, first-rater or gray sheep problems. This paper tries to identify the drawbacks that prevent wide spread use of these methodologies in recommendation. To improve performance of recommendation systems, these methods are combined to form hybrid recommenders. This paper proposes a novel hybrid recommender system which suggests tourism destinations to a user with minimal user interaction. Furthermore, we use sentiment analysis of user’s comments to enhance the efficiency of the proposed system.

scholarly journals Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

2022 ◽  
Vol 34 (3) ◽  
pp. 1-21
Author(s):  
Xue Yu
Keyword(s):  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Target Location ◽  
Geographical Location ◽  
Cold Start ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Interest Point ◽  
Source Information ◽  
Interest Points

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.

scholarly journals A User Preference Tree based Personalized Route Recommendation System for Constraint Tourism and Travel

2021 ◽  
Author(s):  
Kanimozhi U ◽  
Sannasi Ganapathy ◽  
Manjula D ◽  
Arputharaj Kannan
Keyword(s):  
Social Media ◽  
Recommendation System ◽  
Heterogeneous Data ◽  
User Preferences ◽  
Data Sources ◽  
Personalized Recommendation ◽  
Tourist Destination ◽  
Cost Constraints ◽  
Heterogeneous Data Sources ◽  
Route Recommendation

Abstract Personalized recommendation systems recommend the target destination based on user-generated data from social media and geo-tagged photos that are currently available as a most pertinent source. This paper proposes a tourism destination recommendation system which uses heterogeneous data sources that interprets both texts posted on social media and images of tourist places visited and shared by tourists. For this purpose, we propose an enhanced user profile that uses User-Location Vector with LDA and Jaccard Coefficients. Moreover, a new Tourist Destination tree is constructed using the posts extracted from TripAdvisor where each node of the destination tree consists of tourist destination data. Finally, we build a personalized recommendation system based on user preferences, A* algorithm and heuristic shortest path algorithm with cost optimization based on the backtracking based Travelling Salesman Problem solution, tourist destination tree and tree-based hybrid recommendations. Here, the 0/1 knapsack algorithm is used for recommending the best Tourist Destination travel route plans according to the travel time and cost constraints of the tourists. The experimental results obtained from this work depict that the proposed User Centric Personalized destination and travel route recommendation system is providing better recommendation of tourist places than the existing systems by handling multiple heterogeneous data sources efficiently for recommending optimal tour plans with minimum cost and time.

scholarly journals Deep Feedback Network for Recommendation

2020 ◽  
Author(s):  
Ruobing Xie ◽  
Cheng Ling ◽  
Yalong Wang ◽  
Rui Wang ◽  
Feng Xia ◽  
...  
Keyword(s):  
Recommendation System ◽  
User Preferences ◽  
Current Recommendation ◽  
Positive Feedbacks ◽  
Fine Grained ◽  
User Behaviors ◽  
Feedback Network ◽  
Recommendation Algorithms ◽  
Negative Feedbacks ◽  
Interaction Component

Both explicit and implicit feedbacks can reflect user opinions on items, which are essential for learning user preferences in recommendation. However, most current recommendation algorithms merely focus on implicit positive feedbacks (e.g., click), ignoring other informative user behaviors. In this paper, we aim to jointly consider explicit/implicit and positive/negative feedbacks to learn user unbiased preferences for recommendation. Specifically, we propose a novel Deep feedback network (DFN) modeling click, unclick and dislike behaviors. DFN has an internal feedback interaction component that captures fine-grained interactions between individual behaviors, and an external feedback interaction component that uses precise but relatively rare feedbacks (click/dislike) to extract useful information from rich but noisy feedbacks (unclick). In experiments, we conduct both offline and online evaluations on a real-world recommendation system WeChat Top Stories used by millions of users. The significant improvements verify the effectiveness and robustness of DFN. The source code is in https://github.com/qqxiaochongqq/DFN.

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