This paper focuses on reproducing and extending the results of the paper: "Modeling Personalized Item Frequency Information for Next-basket Recommendation" which introduced the TIFU-KNN model and proposed to utilize Personalized Item Frequency (PIF) for Next Basket Recommendation (NBR). We utilized publicly available grocery shopping datasets used in the original paper and incorporated additional datasets to assess the generalizability of the findings. We evaluated the performance of the models using metrics such as Recall@K, NDCG@K, personalized-hit ratio (PHR), and Mean Reciprocal Rank (MRR). Furthermore, we conducted a thorough examination of fairness by considering user characteristics such as average basket size, item popularity, and novelty. Lastly, we introduced novel $β$-VAE architecture to model NBR. The experimental results confirmed that the reproduced model, TIFU-KNN, outperforms the baseline model, Personal Top Frequency, on various datasets and metrics. The findings also highlight the challenges posed by smaller basket sizes in some datasets and suggest avenues for future research to improve NBR performance.