56 research outputs found
PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset
Recommendation systems create personalized list of items that might interest the user
by analyzing the user’s history of past purchases and/or consumption. Generally only
a small subset of the items are assessed by each user, and from the large subset of
unseen items, the systems need to produce an accurate list of recommendations.
For rating based systems, most of the traditional methods for recommendation
focus on the absolute ratings provided by the users to the items. In this work,
we extend the traditional Matrix Factorization approach for recommendation and
propose pairwise relation based factor modeling. We propose the method based on
the pairwise preferences between the items to capture the relative tendency of user
selecting one item over the other.
While modeling the items in the system, the use of pairwise preferences allow
information flow between the items through the preference relations as an additional
information. Item feedbacks are available in the form of reviews apart from the
rating information. The reviews have textual information that can be really helpful
to represent the item’s latent feature vector appropriately. We perform topic modeling
of the item reviews and use the topic vectors to guide the joint factor modeling of the
users and items and learn their final representations. The proposed methods shows
promising results in comparison to the state-of-the-art methods in our experiments.
v
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets
In this paper, we describe a method to pre-
dict emotion intensity in tweets. Our ap-
proach is an ensemble of three regression
methods. The first method uses content-
based features (hashtags, emoticons, elon-
gated words, etc.). The second method
considers word n-grams and character n-
grams for training.
The final method
uses lexicons, word embeddings, word n-
grams, character n-grams for training the
model. An ensemble of these three meth-
ods gives better performance than individ-
ual methods. We applied our method on
WASSA emotion dataset. Achieved re-
sults are as follows: average Pearson cor-
relation is 0.706, average Spearman cor-
relation is 0.696, average Pearson corre-
lation for gold scores in range 0.5 to 1 is
0.539, and average Spearman correlation
for gold scores in range 0.5 to 1 is 0.514
PReFacTO: Preference Relations Based Factor Model with Topic Awareness and Offset
Recommendation systems create personalized list of items that
might interest the user by analyzing the user’s history of past purchases
and/or consumption. For rating based systems, most of the
traditional methods for recommendation focus on the absolute ratings
provided by the users to the items. In this paper, we extend the
traditional Matrix Factorization approach for recommendation and
propose pairwise relation based factor modeling. While modeling
the items in the system, the use of pairwise preferences allow information
flow between the items through the preference relations
as an additional information. Item feedbacks are available in the
form of reviews apart from the rating information. The reviews
have textual information that can be really helpful to represent
the item’s latent feature vector appropriately. We perform topic
modeling of the item reviews and use the topic vectors to guide the
joint factor modeling of the users and items and learn their final
representations. The proposed method shows promising results in
comparison to the state-of-the-art methods in our experiments
Multi-Context Based Neural Approach for COVID-19 Fake-News Detection
When the world is facing the COVID-19 pandemic, society is also fighting another battle to tackle misinformation. Due to the widespread effect of COVID 19 and increased usage of social media, fake news and rumors about COVID-19 are being spread rapidly. Identifying such misinformation is a challenging and active research problem. The lack of suitable datasets and external world knowledge contribute to the challenges associated with this task. In this paper, we propose MiCNA, a multi-context neural architecture to mitigate the problem of COVID-19 fake news detection. In the proposed model, we leverage the rich information of the three different pre-trained transformer-based models, i.e., BERT, BERTweet and COVID-Twitter-BERT to three different aspects of information (viz. general English language semantics, Tweet semantics, and information related to tweets on COVID 19) which together gives us a single multi-context representation. Our experiments provide evidence that the proposed model outperforms the existing baseline and the candidate models (i.e., three transformer architectures) and becomes a state-of-the-art model on the task of COVID-19 fake-news detection. We achieve new state-of-the-art performance on a benchmark COVID-19 fake-news dataset with 98.78% accuracy on the validation dataset and 98.69% accuracy on the test dataset. © 2022 ACM
Travel Package Recommendation
Location Based SocialNetworks (LBSN) benefit the users by allowing them to share their locations and life
moments with their friends. The users can also review the locations they have visited. Classical recommender
systems provide users a ranked list of single items. This is not suitable for applications like trip
planning,where the recommendations should contain multiple items in an appropriate sequence. The
problem of generating such recommendations is challenging due to various critical aspects, which includes
user interest, budget constraints and high sparsity in the available data used to solve the problem.
In this paper, we propose a graph based approach to recommend a set of personalized travel packages.
Each recommended package comprises of a sequence of multiple Point of Interests (POIs). Given the current
location and spatio-temporal constraints, our goal is to recommend a package which satisfies the
constraints. This approach utilizes the data collected fromLBSNs to learn user preferences and also models
the location popularity
Exploiting Meta Attributes for Identifying Event Related Hashtags
Users in social media often participate in discussions regarding different events happening in the physical world (e.g., concerts, conferences, festivals) by posting messages, replying to or forwarding messages related to such events. In various applications like event recommendation, event reporting, etc. it might be useful to find user discussions related to such events from social media. Finding event related hashtags can be useful for this purpose. In this paper, we focus on the problem of finding relevant hashtags for a given event. Features are defined to identify the event related hashtags. We specifically look for features that use similarities of the hashtags with the event metadata attributes. A learning to rank algorithm is applied to learn the importance weights of the features towards the task of predicting the relevance of a hashtag to the given event. We experimented on events from four different categories (namely, Award ceremonies, E-commerce events, Festivals, and Product launches). Experimental results show that our method significantly outperforms the baseline methods
HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process
The prevalence of location-based social networks (LBSNs) has eased the
understanding of human mobility patterns. Knowledge of human dynamics can aid
in various ways like urban planning, managing traffic congestion, personalized
recommendation etc. These dynamics are influenced by factors like social
impact, periodicity in mobility, spatial proximity, influence among users and
semantic categories etc., which makes location modelling a critical task.
However, categories which act as semantic characterization of the location,
might be missing for some check-ins and can adversely affect modelling the
mobility dynamics of users. At the same time, mobility patterns provide a cue
on the missing semantic category. In this paper, we simultaneously address the
problem of semantic annotation of locations and location adoption dynamics of
users. We propose our model HAP-SAP, a latent spatio-temporal multivariate
Hawkes process, which considers latent semantic category influences, and
temporal and spatial mobility patterns of users. The model parameters and
latent semantic categories are inferred using expectation-maximization
algorithm, which uses Gibbs sampling to obtain posterior distribution over
latent semantic categories. The inferred semantic categories can supplement our
model on predicting the next check-in events by users. Our experiments on real
datasets demonstrate the effectiveness of the proposed model for the semantic
annotation and location adoption modelling tasks.Comment: 11 page
Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes
Query auto-completion (QAC) aims at suggesting plausible completions for a
given query prefix. Traditionally, QAC systems have leveraged tries curated
from historical query logs to suggest most popular completions. In this
context, there are two specific scenarios that are difficult to handle for any
QAC system: short prefixes (which are inherently ambiguous) and unseen
prefixes. Recently, personalized Natural Language Generation (NLG) models have
been proposed to leverage previous session queries as context for addressing
these two challenges. However, such NLG models suffer from two drawbacks: (1)
some of the previous session queries could be noisy and irrelevant to the user
intent for the current prefix, and (2) NLG models cannot directly incorporate
historical query popularity. This motivates us to propose a novel NLG model for
QAC, Trie-NLG, which jointly leverages popularity signals from trie and
personalization signals from previous session queries. We train the Trie-NLG
model by augmenting the prefix with rich context comprising of recent session
queries and top trie completions. This simple modeling approach overcomes the
limitations of trie-based and NLG-based approaches and leads to
state-of-the-art performance. We evaluate the Trie-NLG model using two large
QAC datasets. On average, our model achieves huge ~57% and ~14% boost in MRR
over the popular trie-based lookup and the strong BART-based baseline methods,
respectively. We make our code publicly available.Comment: Accepted at Journal Track of ECML-PKDD 202
Preference relations based unsupervised rank aggregation for metasearch
Rank aggregation mechanisms have been used in solving problems from various domains such as bioinformatics, natural language processing, information retrieval, etc. Metasearch is one such application where a user gives a query to the metasearch engine, and the metasearch engine forwards the query to multiple individual search engines. Results or rankings returned by these individual search engines are combined using rank aggregation algorithms to produce the final result to be displayed to the user. We identify few aspects that should be kept in mind for designing any rank aggregation algorithms for metasearch. For example, generally equal importance is given to the input rankings while performing the aggregation. However, depending on the indexed set of web pages, features considered for ranking, ranking functions used etc. by the individual search engines, the individual rankings may be of different qualities. So, the aggregation algorithm should give more weight to the better rankings while giving less weight to others. Also, since the aggregation is performed when the user is waiting for response, the operations performed in the algorithm need to be light weight. Moreover, getting supervised data for rank aggregation problem is often difficult. In this paper, we present an unsupervised rank aggregation algorithm that is suitable for metasearch and addresses the aspects mentioned above.
We also perform detailed experimental evaluation of the proposed algorithm on four different benchmark datasets having ground truth information. Apart from the unsupervised Kendall-Tau distance measure, several supervised evaluation measures are used for performance comparison. Experimental results demonstrate the efficacy of the proposed algorithm over baseline methods in terms of supervised evaluation metrics. Through these experiments we also show that Kendall-Tau distance metric may not be suitable for evaluating rank aggregation algorithms for metasearch
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