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1	Proactive university library book recommender system Mekonnen, Tadesse Zewdu January 2021 (has links) M. Tech. (Department of Information Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Too many options on the internet are the reason for the information overload problem to obtain relevant information. A recommender system is a technique that filters information from large sets of data and recommends the most relevant ones based on people‟s preferences. Collaborative and content-based techniques are the core techniques used to implement a recommender system. A combined use of both collaborative and content-based techniques called hybrid techniques provide relatively good recommendations by avoiding common problems arising from each technique. In this research, a proactive University Library Book Recommender System has been proposed in which hybrid filtering is used for enhanced and more accurate recommendations. The prototype designed was able to recommend the highest ten books for each user. We evaluated the accuracy of the results using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). A measure value of 0.84904 MAE and 0.9579 RMSE found by our system shows that the combined use of both techniques gives an improved prediction accuracy for the University Library Book Recommender System. Hybrid techniques Hybrid filtering Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) Dissertations, Academic -- South Africa Electronic data processing Self-organizing systems
2	Relative or Discounted Cash Flow Valuation on the Fifty Largest US-Based Corporations on Nasdaq : Which of these valuation methods provides the most accurate valuation forecast? Öhrner, Marcus, Öhman, Otto January 2023 (has links) The topic of this Bachelor Thesis is “Which of these valuation methods provides the most accurate valuation forecast”. Assuming that the year is 2020, the goal of this thesis is to forecast the future stock prices of the fifty largest US-based companies on the Nasdaq stock exchange for 2021 and 2022. By using a quantitative method and looking ten years back at historical data. We determine which valuation method provides the most accurate stock price when conducted in a non-sector specific sample by comparing predicted prices to actual stock prices and discussing the results. There are several ways to evaluate a company and the ones being utilized in this thesis are the discounted cash flow valuation method, the price-to-earnings ratio method (equity multiple), and enterprise value to enterprise value before interest, tax, and depreciation (firm multiple). Our results show that when reviewing the valuations of multiple companies in different sectors the relative valuation methods provide better predictions with EV/EBITDA rather than the discounted cash flow method. This thesis provides the reader with a comprehensive overview of these different valuation methods and their effectiveness in providing valuation forecasts. The result of this thesis is beneficial for policymakers, investors, and financial analysts when forecasting future stock prices. Discounted Cash Flow Dividend Discount Model Earnings Before Interest and Taxes Earnings Before Interest Taxes Depreciation and Amortization Depreciation and Amortization ratio Free Cash Flow to Firm Free Cash Flow to Equity Mean Absolute Error Price-to-Earnings Ratio PricewaterhouseCoopers Riskless Rate Root Mean Square Error Return on Capital United States Weighted Average Cost of Capital Business Administration Företagsekonomi

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Proactive university library book recommender system

Relative or Discounted Cash Flow Valuation on the Fifty Largest US-Based Corporations on Nasdaq : Which of these valuation methods provides the most accurate valuation forecast?