Scholarly works

Permanent URI for this collectionhttps://repository.ui.edu.ng/handle/123456789/408

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Author: search.filters.author.Ojo, A.Has files: Yes

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    Leveraging user session for personalized e- commerce recommendation
    (2024-07) Onibonoje, S.; Ojo, A.
    The advent of the internet has propelled many shopping activities online, leading to the rapid growth of e- commerce. This shift has revolutionized the shopping experience, offering unparalleled convenience with anytime, anywhere access via computers and internet connectivity. Moreover, the vast array of easily accessible choices empowers buyers to make well-informed decisions. Numerous websites have emerged to provide e-commerce services, catering either as a complement to physical stores or as standalone businesses. However, the abundance of offerings often leads to information overload for buyers, making product searches time-consuming and frustrating. Personalized e-commerce recommendations alleviate this challenge by guiding users to relevant products swiftly, enhancing the overall shopping experience and ultimately boosting product sales. The study focuses on creating a session-based recommendation system for e-commerce websites, leveraging Recurrent Neural Networks with LSTM architectures to analyze sequential user behavior and browsing context for personalized product recommendations. The research methodology encompasses data collection and preprocessing, where data was splitted into training, testing and validation set. The model was efficiency was evaluated using precision, recall and mean reciprocal rank with the result showing considerable promise for recommendation. This research makes a substantial contribution by suggesting tailored options, users are more likely to find suitable products, leading to increased satisfaction and repeat purchases, thereby benefiting e-commerce platforms.
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    Development of english to yoruba machine translator, using syntax-based model
    (2020-06) Ojo, A.; Obe, O.; Adebayo, A.; Oladunjoye, M.
    Machine translators are required to produce the best possible translation without human assistance. Every machine translator requires programs, automated dictionaries, and grammars to support translation. Studies have shown that the fluency of machine translators depends on the approach or model adopted for their respective developments. Machine translators do not simply involve substituting words in one language for another, but the application of complex linguistic knowledge to decode the contextual meaning of the source text in its entirety. Approaches to machine translators are divided into a single and hybrid approach. In the aim to improve on translation quality of existing English to Yoruba language translator systems, this paper adopts a syntax-based hybrid approach for translating sentences. The grammar for translation is designed and tested with Joshua (an open-source natural language toolkit). The procedure includes data collection, data preparation, data preprocessing, parsing, training of translation model, extract grammar rule, implement grammar, evaluate translations using bilingual evaluation understudy metrics. This paper discusses the translation quality of machine translators (precisely phrase-based and syntax-based) in both tabular and graphical representations. It was observed that a syntax-based translator seemly has higher translation quality than phrase-based.