Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user's preferences and targeted item's features. Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.Comment: This paper has been accepted by the WWW 2018 Conferenc

Optimal precoding for a QoS optimization problem in two-user MISO-NOMA downlink

In this letter, based on the non-orthogonal multiple access (NOMA) concept, a quality-of-service optimization problem for two-user multiple-input-single-output broadcast systems is considered, given a pair of target interference levels. The minimal power and the optimal precoding vectors are obtained by considering its Lagrange dual problem and via Newton's iterative algorithm, respectively. Moreover, the closed-form expressions of the minimal transmission power for some special cases are also derived. One of these cases is termed quasi-degraded, which is the key point and will be discussed in detail in this letter. Our analysis further figures out that the proposed NOMA scheme can approach nearly the same performance as optimal dirty paper coding, as verified by computer simulations

An Optimization Perspective of the Superiority of NOMA Compared to Conventional OMA

While existing works about non-orthogonal multiple access (NOMA) have indicated that NOMA can yield a significant performance gain over orthogonal multiple access (OMA) with fixed resource allocation, it is not clear whether such a performance gain will diminish when optimal resource (Time/Frequency/Power) allocation is carried out. In this paper, the performance comparison between NOMA and conventional OMA systems is investigated, from an optimization point of view. Firstly, by using the idea of power splitting, a closed-form expression for the optimum sum rate of NOMA systems is derived. Then, with rigorous mathematical proofs, we reveal the fact that NOMA can always outperform conventional OMA systems, even if both are equipped with the optimal resource allocation policies. Finally, computer simulations are conducted to validate the accuracy of the analytical results.Comment: 28 pages, 8 figures, submitted to IEEE Transactions on Signal Processin

On the application of quasi-degradation to MISO-NOMA downlink

In this paper, the design of non-orthogonal multiple access (NOMA) in a multiple-input-single-output (MISO) downlink scenario is investigated. The impact of the recently developed concept, quasi-degradation, on NOMA downlink transmission is first studied. Then, a Hybrid NOMA (H-NOMA) precoding algorithm, based on this concept, is proposed. By exploiting the properties of H-NOMA precoding, a low-complexity sequential user pairing algorithm is consequently developed, to further improve the overall system performance. Both analytical and numerical results are provided to demonstrate the performance of the H-NOMA precoding through the average power consumption and outage probability, while conventional schemes, as dirty-paper coding and zero-forcing beamforming, are used as benchmarking

The FruitShell French synthesis system at the Blizzard 2023 Challenge

This paper presents a French text-to-speech synthesis system for the Blizzard Challenge 2023. The challenge consists of two tasks: generating high-quality speech from female speakers and generating speech that closely resembles specific individuals. Regarding the competition data, we conducted a screening process to remove missing or erroneous text data. We organized all symbols except for phonemes and eliminated symbols that had no pronunciation or zero duration. Additionally, we added word boundary and start/end symbols to the text, which we have found to improve speech quality based on our previous experience. For the Spoke task, we performed data augmentation according to the competition rules. We used an open-source G2P model to transcribe the French texts into phonemes. As the G2P model uses the International Phonetic Alphabet (IPA), we applied the same transcription process to the provided competition data for standardization. However, due to compiler limitations in recognizing special symbols from the IPA chart, we followed the rules to convert all phonemes into the phonetic scheme used in the competition data. Finally, we resampled all competition audio to a uniform sampling rate of 16 kHz. We employed a VITS-based acoustic model with the hifigan vocoder. For the Spoke task, we trained a multi-speaker model and incorporated speaker information into the duration predictor, vocoder, and flow layers of the model. The evaluation results of our system showed a quality MOS score of 3.6 for the Hub task and 3.4 for the Spoke task, placing our system at an average level among all participating teams