scholarly journals Ontology-Based Diet Recommendation System

2019 ◽  
Vol 8 (3) ◽  
pp. 5811-5815
Keyword(s):  
Query Processing ◽  
Recommender Systems ◽  
Recommendation System ◽  
Selection Process ◽  
Diet Selection ◽  
Nutrient Requirements ◽  
Food Items

Recommender systems are needed to find food items of one’s interest. We propose a food personalization framework that assists the user with the actual diet selection process. The Ontology-based process, recommend an appropriate diet for the user. The system analyses the user’s queries based on their requirements and recommends diet, based on their diseases and deficiencies. The system is tested for its efficiency in terms of query processing for users nutrient requirements.

Legal document recommendation system: A cluster based pairwise similarity computation

2021 ◽  
pp. 1-13
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti Rana
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Real Life ◽  
Citation Network ◽  
Search Space ◽  
Pairwise Similarity ◽  
Large Numbers ◽  
Legal Document ◽  
Legal Domain ◽  
Similarity Scores

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.

scholarly journals Optimising the selection of food items for FFQs using Mixed Integer Linear Programming

2014 ◽  
Vol 18 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Johanna C Gerdessen ◽  
Olga W Souverein ◽  
Pieter van ‘t Veer ◽  
Jeanne HM de Vries
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Selection Process ◽  
Saturated Fat ◽  
Mixed Integer ◽  
Monounsaturated Fat ◽  
Food Items ◽  
Milp Model ◽  
Selection Of

AbstractObjectiveTo support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible.DesignSelection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium.ResultsThe food lists generated by the MILP model have good performance in terms of length, coverage and R2 (explained variance) of all nutrients. MILP-generated food lists were 32–40 % shorter than a benchmark food list, whereas their quality in terms of R2 was similar to that of the benchmark.ConclusionsThe results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

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.

Black-tailed deer feeding bouts: dynamic events

1989 ◽  
Vol 67 (6) ◽  
pp. 1353-1362 ◽  
Author(s):  
Michael P. Gillingham ◽  
Fred L. Bunnell
Keyword(s):  
Food Preference ◽  
Odocoileus Hemionus ◽  
Diet Selection ◽  
Odocoileus Hemionus Columbianus ◽  
Food Items ◽  
Dynamic Events ◽  
Feeding Station ◽  
Ad Libitum ◽  
Preferred Foods ◽  
First Time

Foraging bouts of captive black-tailed deer (Odocoileus hemionus columbianus Richardson) were investigated to examine how searching for food affects diet selection. We determined food preference for three types of food under ad libitum conditions and then studied the foraging of two deer in a 0.5-ha, vegetation-free pen in which we controlled food availability and distribution of the same three types of food. Our hypotheses included the following: (i) clumping of preferred food into patches would enable animals to better exploit food distributions; (ii) the switch from preferred to lower-ranked food would be gradual as preferred food was less frequently encountered; and (iii) deer would respond to a lower abundance of preferred foods by eating more of lower-ranked food items at each feeding location. Searching for food alone did not alter diet selection from ad libitum conditions. Deer nearly exhausted their highly preferred food item before switching to lower-ranked ones. Amount of preferred food already eaten during a trial was positively correlated with the time that animals continued searching before switching to lower-ranked food items. Switching was related to amount and type of food encountered and not to amount of food in the pen. Clumping of the preferred food had no significant effect on the amount of food eaten, but did significantly influence types of food encountered by one deer. When preferred food was abundant, it was not always completely eaten the first time a feeding platform was visited. Increases in the intake rates of nonpreferred food items resulted from deer visiting more feeding stations containing nonpreferred food items and not from deer eating more food at each feeding station.

DSS for Web Mining Using Recommendation System

2017 ◽  
pp. 22-34 ◽  
Author(s):  
Varaprasad Rao M ◽  
Vishnu Murthy G
Keyword(s):  
Recommender Systems ◽  
Web Mining ◽  
Recommendation System ◽  
User Behavior ◽  
Web Content ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
Computer Based ◽  
The Many

Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

The interactive effects of novel food flavours and food composition on the diet selection of sheep

2001 ◽  
Vol 2001 ◽  
pp. 61-61
Author(s):  
G. Arsenos ◽  
I. Kyriazakis
Keyword(s):  
Feeding Behaviour ◽  
Nutrient Content ◽  
Diet Selection ◽  
Food Composition ◽  
Interactive Effects ◽  
Nutrient Requirements ◽  
Organoleptic Properties ◽  
Novel Food ◽  
Selection Of

Animals have predispositions towards the organoleptic properties, such as flavours, of the foods available to them. These predispositions can influence the feeding behaviour and diet selection of animals and prevent from, or enable them to select a diet that meets their nutrient requirements, in both short - and longer - run (Forbes and Kyriazakis, 1995). In this experiment, we investigated whether predispositions of sheep for novel food flavours could affect their diet selection when offered a choice between two foods with different nutrient content. The specific hypotheses tested were that such predispositions are: (i ) influenced by the nutritional quality of the food that are associated with, and (ii) affected by the current nutritional state of the animal.

Diet and diet selection of two species in the macropodid browser–grazer continuum— do they eat what they 'should'?

2002 ◽  
Vol 50 (2) ◽  
pp. 183 ◽  
Author(s):  
J. A. Sprent ◽  
C. McArthur
Keyword(s):  
Plant Biomass ◽  
Diet Selection ◽  
Pine Plantation ◽  
Feeding Patterns ◽  
Diet Diversity ◽  
Food Items ◽  
Plant Groups ◽  
Dietary Components ◽  
Selection Of

On the basis of their dentition, species of Macropus are predicted to be grazers and species of Thylogale are predicted to be browsers. We tested these predictions by comparing diet and diet selection of the red-necked wallaby (Macropus rufogriseus rufogriseus) and red-bellied pademelon (Thylogale billardierii), by analysing forestomach contents of animals that had fed in the same region of a young pine plantation. Grasses, followed by broad-leafed forbs, were the most abundant plant groups in the field (together comprising 71% of the plant biomass), and were also the main dietary components of both macropodid species (91%). No differences were detected in diet of the two species when summarised in terms of diet diversity, evenness or overlap. When diet selection was compared, however, distinct differences were found between the two species. Red-necked wallabies selected for grasses (74% of the diet compared with 55% in the field) whereas red-bellied pademelons selected for broad-leafed forbs (38% of the diet compared with 16% in the field). Feeding patterns were therefore consistent with dietary predictions, provided diet selection was considered rather than simply diet. Diet selection is more appropriate for testing dietary predictions, because it reflects animals' attempts to consume food items that they prefer, that is, that they are functionally suited to consuming, even when such items are not as abundant as less preferred food.

Diet selection in Arctic lemmings (Lemmus sibericus and Dicrostonyx groenlandicus): food preferences

1985 ◽  
Vol 63 (5) ◽  
pp. 1161-1173 ◽  
Author(s):  
Arthur R. Rodgers ◽  
Martin C. Lewis
Keyword(s):  
Selection Process ◽  
Food Preferences ◽  
Secondary Compounds ◽  
Diet Selection ◽  
Caloric Content ◽  
Dicrostonyx Groenlandicus ◽  
In Captivity ◽  
Preference Patterns ◽  
The Stability ◽  
Physical And Chemical

Testing of hypotheses relating lemming population dynamics to their food supply requires a detailed understanding of several major components of the diet selection process such as requirements, availability, preference, and selectivity. In this study, food preferences of Arctic lemmings were determined in cafeteria trials: Lemmus preferred graminoids and moss, while Dicrostonyx preferred shrubs and herbs. The stability of these preference patterns in each species was tested in further experimental cafeteria trials. Individuals of both species were pretreated on one of several diets, and two main types of trial were conducted involving limited and unlimited availability of test foods. Naive animals of both species, born and raised in captivity on artificial diets, were also used in the trials. In all cases, preference patterns in each species were maintained, suggesting that they are strongly heritable. Comparison of preference indices to the physical and chemical characteristics of tundra plants indicates that preference patterns in both species are related primarily to macronutrients and caloric content. Differences between Lemmus and Dicrostonyx are determined by secondary compounds and the physical characteristics of the plant species preferred by each. Comparison of ingestion rates and digestibility coefficients indicate that Dicrostonyx has a greater capacity than Lemmus in dealing with the negative characteristics of plants, such as secondary compounds or the presence of plant "hairs."

Enhancing Recommender Diversity Using Gaussian Cloud Transformation

2015 ◽  
Vol 23 (04) ◽  
pp. 521-544 ◽  
Author(s):  
Jinpeng Chen ◽  
Yu Liu ◽  
Deyi Li
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Data Sets ◽  
Complete Spectrum ◽  
Unified Framework ◽  
Real World Data ◽  
Balance Accuracy ◽  
Average Accuracy ◽  
Novel Method ◽  
Attention To Diversity

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.

scholarly journals An Automated Music Recommendation System Based on Listener Preferences

2021 ◽  
Author(s):  
Mukkamala. S.N.V. Jitendra ◽  
Y. Radhika
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Software Tool ◽  
Vital Role ◽  
Music Recommendation ◽  
The World ◽  
Different Types ◽  
Music Recommendation System

Recommender systems play a vital role in e-commerce. It is a big source of a market that brings people from all over the world to a single place. It has become easy to access and reach the market while sitting anywhere. Recommender systems do a major role in the commerce mobility go smoothly easily as it is a software tool that helps in showing or recommending items based on user’s preferences by analyzing their taste. In this paper, we make a recommender system that would be specifically for music applications. Different people listen to different types of music, so we make note of their taste in music and suggest to them the next song based on their previous choice. This is achieved by using a popularity algorithm, classification, and collaborative filtering. Finally, we make a comparison of the built system for its effectiveness with different evaluation metrics.

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