Linqia combines the science and accountability of online media with award-winning service to deliver guaranteed influencer marketing results for our clients

Linqia

linqia

As growth hacker for and previous as CTO for , I often face tasks in the form of “take this Excel file with user emails and retrieve column X, Y & Z from the database” (like name or total order value). Tessin.se Offerta.se I’ve been looking for a practical solution where this can be done in an efficient way without having to write custom code each time. After a few iterations, this is what I came up with: I’m a big fan of the .Net scratchpad . This application makes is super simple to query a database using C# and linq. And since I prefer C# over VBA this is exactly the tool I want to use to solve the problem. LINQPad A first attempt was to write a script in LINQpad that opens a XLS file and reads the input value (email) from one column and then outputs the results in other columns. This is not very flexible though. The holy grail for me was to have a way to create ad-hoc formulas inExcel that take an input such as an email and return a specified value from the database. To do this I created a small self hosted JSON REST service using the in a LINQPad script: Nancy Web Framework When running this script a small web service is started locally on your computer: Next step is to connect this REST API with Excel. To do this I’m using a tool that I wrote called . This is an Excel add-on (not free but with a 14 day trial) that has a bunch of features useful for growth hacking. For this solution, I’m using the function to fetch the JSON response from the REST service we wrote and with the query language get a piece of the structure: SeoTools for Excel JsonPathOnUrl JSONPath =JsonPathOnUrl(" "&A2;"firstName") http://localhost:8090/user-by-email/ Now we can easily get all the values we need. SeoTools takes care of caching so that the same request isn’t executed more than once. The Nancy REST service can easily be modified for whatever information you need to retrieve from the database. This takes a bit of setup, but once you have it up and running it works like a charm. If you don’t want to use SeoTools it’s not that difficult to find a VBA solution. I also used this technique if I have a SOAP service (there’s a SOAP driver for LINQPad 4) that I want to query using Excel. I’m not a VBA fan at all so if I have anything that needs scripting (string manipulations etc.) then it’s a lot easier for me to write this code in C# in LINQPad. This is definitely on my top-10 list of productivity hacks that saves me time every day! Please let me know what you think or how you would have solved this. LINQPad boilerplate code You also need to add the Nuget package to your LINQPad script and include it’s namespaces. Nancy.Hosting.Self

My holy grail: Connect Excel with LINQPad

Sentiment Analysis through LLM Negotiations: Conclusion and References

Authors: (1) Yunshan Ma, National University of Singapore; (2) Xiaohao Liu, University of Chinese Academy of Sciences; (3) Yinwei Wei, Monash University; (4) Zhulin Tao, Communication University of China and a Corresponding author; (5) Xiang Wang, University of Science and Technology of China and affiliated with Institute of Artificial Intelligence, Institute of Dataspace, Hefei Comprehensive National Science Center; (6) Tat-Seng Chua, National University of Singapore. Table of Links Abstract & Introduction Methodology Experiments Related Work Conclusion and Future Work, Acknowledgment and References 5 CONCLUSION AND FUTURE WORK In this work, we systematically study the problem of bundle construction and define a more comprehensive formulation by considering all the three types of data, i.e., multimodal features, item-level user feedback data, and existing bundles. Based on this formulation, we highlight two challenges: 1) how to learn expressive bundle representations given multiple features; and 2) how to counter the modality missing, noise, and sparity problem. To tackle these challenges, we propose a novel method of Contrastive Learningenhanced Hierarchical Encoder (CLHE) for bundle construction. Our method beats a list of leading methods on four datasets of two application domains. Extensive ablation and model studies justify the effectiveness of the key modules. Despite the great performance that has been achieved by this work, there is still large space to be explored for bundle construction. First, the current evaluation setting is a little bit rigid and inflexible, it is interesting to extend it to more flexible setting to align with real applications. For example, given arbitrary number of seed items, the model is asked to construct the bundle. Second, some of the feature extractors are pre-trained and fixed, i.e., the multimodal feature extraction and user-item interaction models. Is it possible to optimize these feature extractors in an end-to-end fashion thus the extracted features would be more aligned to the bundle construction task? Finally, this work just targets at unpersonalized bundle construction. It is an interesting and natural direction to push forward this work to personalized bundle construction. ACKNOWLEDGEMENT This research is supported by NExT Research Center, National Natural Science Foundation of China (9227010114), and the University Synergy Innovation Program of Anhui Province (GXXT-2022-040). REFERENCES [1] Jinze Bai, Chang Zhou, Junshuai Song, Xiaoru Qu, Weiting An, Zhao Li, and Jun Gao. 2019. Personalized Bundle List Recommendation. In WWW. ACM, 60–71. [2] Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. 2011. The Million Song Dataset. In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011). [3] Tzoof Avny Brosh, Amit Livne, Oren Sar Shalom, Bracha Shapira, and Mark Last. 2022. BRUCE: Bundle Recommendation Using Contextualized item Embeddings. In RecSys. ACM, 237–245. [4] Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Shunzhi Zhu, and Tat-Seng Chua. 2017. Embedding Factorization Models for Jointly Recommending Items and User Generated Lists. In SIGIR. ACM, 585–594. [5] Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2021. Bundle Recommendation and Generation with Graph Neural Networks. IEEE Transactions on Knowledge and Data Engineering (2021). [6] Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2023. Bundle Recommendation and Generation With Graph Neural Networks. IEEE Trans. Knowl. Data Eng. 35, 3 (2023), 2326–2340. [7] Ching-Wei Chen, Paul Lamere, Markus Schedl, and Hamed Zamani. 2018. Recsys challenge 2018: automatic music playlist continuation. In RecSys. ACM, 527–528. [8] Ke Chen, Xingjian Du, Bilei Zhu, Zejun Ma, Taylor Berg-Kirkpatrick, and Shlomo Dubnov. 2022. HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection. In ICASSP. IEEE, 646–650. [9] Liang Chen, Yang Liu, Xiangnan He, Lianli Gao, and Zibin Zheng. 2019. Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network.. In IJCAI. 2095–2101. [10] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In ICML (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597–1607. [11] Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, and Binqiang Zhao. 2019. POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion. In KDD. ACM, 2662–2670. [12] Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, and Liang Wang. 2019. Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks. In WWW. ACM, 307–317. [13] Qilin Deng, Kai Wang, Minghao Zhao, Runze Wu, Yu Ding, Zhene Zou, Yue Shang, Jianrong Tao, and Changjie Fan. 2021. Build Your Own Bundle - A Neural Combinatorial Optimization Method. In ACM MM. ACM, 2625–2633. [14] Qilin Deng, Kai Wang, Minghao Zhao, Zhene Zou, Runze Wu, Jianrong Tao, Changjie Fan, and Liang Chen. 2020. Personalized Bundle Recommendation in Online Games. In CIKM. ACM, 2381–2388. [15] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1). Association for Computational Linguistics, 4171–4186. [16] Yujuan Ding, Yunshan Ma, Wai Keung Wong, and Tat-Seng Chua. 2021. Leveraging Two Types of Global Graph for Sequential Fashion Recommendation. In ICMR. ACM, 73–81. [17] Yujuan Ding, PY Mok, Yunshan Ma, and Yi Bin. 2023. Personalized fashion outfit generation with user coordination preference learning. Information Processing & Management 60, 5 (2023), 103434. [18] Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. SimCSE: Simple Contrastive Learning of Sentence Embeddings. In EMNLP (1). Association for Computational Linguistics, 6894–6910. [19] Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS (JMLR Proceedings, Vol. 9). JMLR.org, 249–256. [20] Weili Guan, Haokun Wen, Xuemeng Song, Chung-Hsing Yeh, Xiaojun Chang, and Liqiang Nie. 2021. Multimodal Compatibility Modeling via Exploring the Consistent and Complementary Correlations. In ACM MM. ACM, 2299–2307. [21] Xintong Han, Zuxuan Wu, Yu-Gang Jiang, and Larry S. Davis. 2017. Learning Fashion Compatibility with Bidirectional LSTMs. In ACM MM. ACM, 1078–1086. [22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR. IEEE Computer Society, 770–778. [23] Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR. ACM, 639–648. [24] Yun He, Yin Zhang, Weiwen Liu, and James Caverlee. 2020. Consistency-Aware Recommendation for User-Generated Item List Continuation. In WSDM. ACM, 250–258. [25] Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). [26] Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D. Plumbley. 2020. PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition. IEEE ACM Trans. Audio Speech Lang. Process. 28 (2020), 2880–2894. [27] Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi. 2022. BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. In ICML (Proceedings of Machine Learning Research, Vol. 162). PMLR, 12888–12900. [28] Ming Li, Lin Li, Qing Xie, Jingling Yuan, and Xiaohui Tao. 2022. MealRec: A Meal Recommendation Dataset. CoRR abs/2205.12133 (2022). [29] Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, and Tat-Seng Chua. 2020. Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. In SIGIR. ACM, 159–168. [30] Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, and Xiuqiang He. 2022. PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation. In WWW (Companion Volume). ACM, 62–66. [31] Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In WWW. ACM, 689–698. [32] Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, and Shanika Karunasekera. 2018. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowl. Inf. Syst. 54, 2 (2018), 375–406. [33] Yusan Lin, Maryam Moosaei, and Hao Yang. 2020. OutfitNet: Fashion Outfit Recommendation with Attention-Based Multiple Instance Learning. In WWW. ACM / IW3C2, 77–87. [34] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs/1907.11692 (2019). [35] Yong Liu, Susen Yang, Chenyi Lei, Guoxin Wang, Haihong Tang, Juyong Zhang, Aixin Sun, and Chunyan Miao. 2021. Pre-training Graph Transformer with Multimodal Side Information for Recommendation. In ACM MM. ACM, 2853– 2861. [36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In ICCV. IEEE, 9992–10002. [37] Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, and Tat-Seng Chua. 2022. CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation. In KDD. ACM, 1233–1241. [38] Yuyang Ren, Haonan Zhang, Luoyi Fu, Xinbing Wang, and Chenghu Zhou. 2023. Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation. In SIGIR. ACM, 1660–1669. [39] Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In WWW. ACM, 811–820. [40] Rohan Sarkar, Navaneeth Bodla, Mariya I. Vasileva, Yen-Liang Lin, Anurag Beniwal, Alan Lu, and Gerard Medioni. 2023. OutfitTransformer: Learning Outfit Representations for Fashion Recommendation. In WACV. IEEE, 3590–3598. [41] Xuemeng Song, Shi-Ting Fang, Xiaolin Chen, Yinwei Wei, Zhongzhou Zhao, and Liqiang Nie. 2023. Modality-Oriented Graph Learning Toward Outfit Compatibility Modeling. IEEE Trans. Multim. 25 (2023), 856–867. [42] Zhu Sun, Jie Yang, Kaidong Feng, Hui Fang, Xinghua Qu, and Yew Soon Ong. 2022. Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling. In SIGIR. ACM, 2900–2911. [43] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998–6008. [44] Penghui Wei, Shaoguo Liu, Xuanhua Yang, Liang Wang, and Bo Zheng. 2022. Towards Personalized Bundle Creative Generation with Contrastive NonAutoregressive Decoding. In SIGIR. ACM, 2634–2638. [45] Yinwei Wei, Wenqi Liu, Fan Liu, Xiang Wang, Liqiang Nie, and Tat-Seng Chua. 2023. LightGT: A Light Graph Transformer for Multimedia Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1508–1517. [46] Yinwei Wei, Xiaohao Liu, Yunshan Ma, Xiang Wang, Liqiang Nie, and Tat-Seng Chua. 2023. Strategy-aware Bundle Recommender System. In SIGIR. ACM, 1198– 1207. [47] Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019. MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video. In ACM MM. ACM, 1437–1445. [48] Yinwei Wei, Xiang Wang, Liqiang Nie, Shaoyu Li, Dingxian Wang, and Tat-Seng Chua. 2022. Causal inference for knowledge graph based recommendation. IEEE Transactions on Knowledge and Data Engineering (2022). [49] Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In SIGIR. ACM, 726–735. [50] Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov. 2022. Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation. CoRR abs/2211.06687 (2022). [51] Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems. In WSDM. ACM, 153–162. [52] Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, and Tat-Seng Chua. 2019. TransNFCM: Translation-Based Neural Fashion Compatibility Modeling. In AAAI. AAAI Press, 403–410. [53] Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2022. XSimGCL: Towards extremely simple graph contrastive learning for recommendation. arXiv preprint arXiv:2209.02544 (2022). [54] Zhouxin Yu, Jintang Li, Liang Chen, and Zibin Zheng. 2022. Unifying multiassociations through hypergraph for bundle recommendation. Knowl. Based Syst. 255 (2022), 109755. [55] Sen Zhao, Wei Wei, Ding Zou, and Xianling Mao. 2022. Multi-View Intent Disentangle Graph Networks for Bundle Recommendation. In AAAI. AAAI Press, 4379–4387. This paper is under CC 4.0 license. available on arxiv

CLHE: A Breakthrough in Bundle Construction - Challenges Overcome and Paths Ahead

Topographical Notes

Physical Geography

Linear Perspective

Data persistence plays a big role in almost every application there is. With the introduction of .NET Core and EntityFramework Core a new possibility arose when it comes to data persistence. Now, it is possible to use EntityFramework Core to generate a local storage for our Xamarin Applications. In this article we will explain in a step-by-step tutorial how to set up a Xamarin Forms App with a SQLite local storage, and we are going to use EntityFramework Core to generate a Code-First Database and apply that migration to our app. I assume that you have basic knowledge and have installed the .NET Core SDK, .NET Core tools EntityFramework Core, and all other tools needed to develop a Xamarin Forms app. If not, I suggest you get familiar with them first. I am using Rider as my IDE. Note: Resources: https://docs.microsoft.com/en-us/ef/core/ https://docs.microsoft.com/en-us/ef/core/miscellaneous/cli/dotnet https://docs.microsoft.com/en-us/ef/ef6/modeling/code-first/migrations/ Setting up the project Let us go ahead and create a new Xamarin Forms App. Nothing fancy here, just do the usual project creation from the wizard. My project will be called XamarinEFCore. install and in every project, the traditional and the class library. Next: Microsoft.EntityFrameworkCore Microsoft.EntityFrameworkCore.Sqlite Then, create a new class library which will contain all the class entities we will store in our database. when you select the target framework for the class library, it should be .NET Standard 2.0 and not .NETCore x.x .Also install the here as well. Be careful Microsoft.EntityFrameworkCore You will need it for data annotations. Next, we will create our first entity. It is a pretty basic one just for demo purposes. public {
   [DatabaseGenerated(DatabaseGeneratedOption.Identity)]
   public int Id { get; set; }
   public string RandomGuid { get; set; }
   public DateTime TimeAdded { get; set; }
} class ListItem The data annotation on top of the Id property serves to generate an auto-increment primary key. If you plan to handle unique keys yourself, then you can use the annotation. [Key] Setting up the DatabaseContext The next step is to reference the entities class project in the traditional class library. This is needed so when we setup the context we can define the data sets. Then, we create a folder called that will contain our context class. Our context class will look like this: DatabaseContext public {
    public DbSet<ListItem> Items { get; set; }
    private string DatabaseName = ;

    protected override OnConfiguring(DbContextOptionsBuilder optionsBuilder)
    { databasePath; (Device.RuntimePlatform)
        { Device.iOS:
                databasePath = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.MyDocuments), , , DatabaseName); ; Device.Android:
                databasePath = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.Personal), DatabaseName); ; : NotImplementedException( );
        }
        optionsBuilder.UseSqlite($ );
    }
} : class ApplicationDbContext DbContext const "myItems.db" void String switch case ".." "Library" break case break default throw new "Platform not supported" "Filename={databasePath}" We add a property for each entity that we will store in the database. Because every platform has a different storage location, we set the databasePath accordingly based on the device platform. DbSet< Class > Next, we must do some work in the iOS project to make this work. In the add this line before AppDelegate.cs Xamarin.Forms.Init(); SQLitePCL.Batteries_V2.Init (); On the file add these two lines on top of the namespace: Main.cs [assembly: Preserve( (System.Linq.Queryable), AllMembers = )]
[assembly: Preserve( (System.DateTime), AllMembers = )]
[assembly: Preserve( (System.Linq.Enumerable), AllMembers = )]

[assembly: Preserve( (System.Linq.IQueryable), AllMembers = )] typeof true typeof true typeof true typeof true We need to tell the linker explicitly to include these assemblies, because it has no other way to know that EntityFramework Core needs them. If we don’t, the application will crash while we are migrating the database or if we try to use LINQ queries for data filtering. As a last step, open the file and in the constructor add these two lines: App.xaml.cs db = ApplicationDbContext();

db.Database.Migrate(); var new This applies all the pending migrations to the database, and if it doesn’t exist it creates the database. Creating a migration To create a migration for our app to use we cannot use any of these projects sadly, so we need to create a new project in the same solution. Let us create a console application this time that targets .NETCore so we can create a migration. The next step is to change the default namespace the project has, to match the one of the shared class library. In my example, the namespace will be changed to XamarinEFCore. , and . Then we install: Microsoft.EntityFrameworkCore Microsoft.EntityFrameworkCore.Sqlite Microsoft.EntityFrameworkCore.Design We also need to add a reference to our Entites project. Next, we will recreate the DatabaseContext folder and the ApplicationDbContext class in the MigrationCreator project. The only difference will be in the overridden method. It should look like this. protected override OnConfiguring(DbContextOptionsBuilder optionsBuilder)
{
    optionsBuilder.UseSqlite ($ );
} void "Filename={DatabaseName}" You are done! Congratulations Now you only need to create the migration and output it to the traditional shared library of the application. You do that from the command line. You navigate to the MigrationCreator folder which contains your .csproj file and execute the following command dotnet ef migrations add -o <path-to-your-shared-lib> < > MigrationName All set! Happy coding. The full source code for the application sample can be found here. Also published at: https://blog.codechem.com/using-entityframework-core-and-sqlite-with-xamarin-forms

Stories, News & Mentions about LINQIA

Linqia’s Stories on HackerNoon